{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "36fb9562-79b4-4ea4-825d-ba95f2ed6c1b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import wfdb\n",
    "from scipy.io import loadmat\n",
    "import glob\n",
    "import os\n",
    "import collections\n",
    "from shutil import copyfileobj\n",
    "from tqdm import tqdm\n",
    "import joblib\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "import warnings; warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2644105b-91df-47c6-b150-9417f998ef47",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "SNOMED_CODES = {\n",
    "    '426783006': 'NORM', #1752\n",
    "    '164889003': 'AF', #570\n",
    "    '270492004': 'IAVB', #769\n",
    "    '426177001': 'SB', #1677\n",
    "    '39732003': 'LAD', #940\n",
    "    '427084000': 'STach', #1261\n",
    "    '164934002': 'TAb', #2306\n",
    "    '59931005': 'TInv', #812\n",
    "    '111975006': 'LQT', #1391\n",
    "    '284470004': 'PAC', #639\n",
    "}\n",
    "\n",
    "CLASSES = ['NORM', 'AF', 'IAVB', 'SB', 'LAD', 'STach', 'TAb', 'TInv', 'LQT', 'PAC']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "19033994-a70f-4c92-925c-f2f418371447",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "data_path = './data/original/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dfc98e55-eac8-45a6-bd62-335af620d74c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f869f2d5-271a-4d8d-9abe-7c4d62b8067d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # remove the .mat in the first line of all headers\n",
    "\n",
    "# headers = glob.glob(data_path+'*.hea')\n",
    "# for h in headers:\n",
    "#     with open(h, 'r') as f:\n",
    "#         lines = f.readlines()\n",
    "\n",
    "#     lines[0] = lines[0].replace('.mat', '')\n",
    "\n",
    "#     with open(h, 'w') as f:\n",
    "#         f.writelines(lines)        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d469f2a-cf49-4575-b013-0e50d9ef3463",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f067ff1b-7017-472a-9262-2957f8a224b8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./data/original/E01720'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "headers[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7738fd42-f399-48c3-ae11-2f1368f1cdf7",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000, 12)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "signal, meta = wfdb.rdsamp('./data/original/E01720')\n",
    "\n",
    "signal.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b59fcb43-5763-400b-8269-d730995d6722",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd9fa2a1430>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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g7eva1qurC1NH3sgE/ngZ+OXxcB9JRKG7wHn55ZcxZcoUTJo0CT169MBbb72FhIQELFiwQPUzFosFqamp3ldKSop3H8dxeOWVV/D444/j6quvRp8+ffDRRx/h+PHj+Pbbb/X+OfXHpYPZ88wB7euUo7VFhMz5UooN+A/Ngp69aF1nE6drWsLupcCCkcAbWfp9R9Fu/eo243944q9wH0FEoavAqa2tRV5eHrKzs8UvtFqRnZ2NtWvXqn6uoqIC7dq1Q0ZGBq6++mrs2LHDu+/AgQMoKCiQ1JmUlITMzEzVOu12O8rKyiSvsLDqWeC5dtrflKYc2qh3b9dkLiqzudR0xaR5cIzK32MWdizhlxUF+n2HrveNCc95tC3cRxBR6CpwTp06BZfLJbHAAEBKSgoKCpQv+q5du2LBggX47rvv8Mknn8DtdmPw4ME4evQoAHg/F0qdc+fORVJSkveVkZFR359WN1bNBWorgF8e07beqBht61PEZBYc3QWDxsdMeXVE9BQhklngTZToz4wYEqRrosEP5QXAZzcA+5ZrWy+L1gKH44ClDwK/vahtvQYRca1qVlYWJk6ciH79+uGiiy7CN998g5YtW+Ltt9+uc50zZ85EaWmp93XkyBENj7gOaH3jW40QOBpj9hgczSELjhezjnQyLL7HJBghcHR1nWsscH58ANjzE/DJWG3rZdFa4BTuADa+B6yYo229BqGrwGnRogWioqJQWFgo2V5YWIjU1NSg6oiJiUH//v2xb98+APB+LpQ6bTYbEhMTJa+wovUQZiNcVLo2JNpWDUB/C47WD0fSN8qYSYToPTLQbJjRgqOnwDllwEhfrTu7jmpt6zMYXQVObGwsBgwYgNzcXO82t9uN3NxcZGUFF3jmcrmwbds2pKWlAQA6dOiA1NRUSZ1lZWVYv3590HWGH40bPzO6qEwfg6P1MZPC8WJYkLGeLioTjcAxM5p3vHT8Dx1V2tZnBJK8QOa7pnWfi2r69Om4+eabMXDgQAwaNAivvPIKKisrMWnSJADAxIkT0bp1a8ydOxcAMHv2bJx//vno3LkzSkpK8MILL+DQoUO47bbbAPAjrO6//348/fTT6NKlCzp06IAnnngC6enpGDNmjN4/Rxu0vnHYiR/dbsAacZ5HX8weg6O5BccE/5lhmDUPDgUZG47W942eAqe2Utv6FNG6XWLaUc6FCIxq8YvuAmfcuHE4efIkZs2ahYKCAvTr1w85OTneIOHDhw/DyjyQz5w5gylTpqCgoABNmzbFgAED8Oeff6JHjx7eMo888ggqKysxdepUlJSUYMiQIcjJyfFJCBixaN67M+FFSDE4UmgUlYhhczqZKL6H7hFl2M6dFujponLrMOee3rDtkttpkLdAOwyZTXzatGmYNm2a4r5Vq1ZJ3v/vf//D//73P7/1WSwWzJ49G7Nnz9bqEI1Fz96/22WOi9D0OUPIRaUfJh0mTon+jEfreEazJvoT0PyaZgWO+QSaCbr6DRBdXVQ6Bfbp6es2ZQyOxpAFR8S0FhyTBhmXHAEO/hHuo6gbmruoyAonQW7BMRkkcMKC1g8zIy5CPfNNmDAGR3PMdrw6oqUIqZUFdrK9UD0DmDUXT9pWJ+GVXsDC0cDRPG3rNZPI82JSkerFRHFlBkACJxxo/fCVu6jMgK45QwBdBIOePXQKMmbQ6DzvXQ7MbQOsf4epzyDXqBl7/8c26f8dmqPjMHFyQ8Psgo9a1YaGXn5jXYdjmsSCo+cNTgJHRItr41gesGgsH3T/88NMfUa5qEwYv6F1PIsZMb2LSs9QAvNBrWo40Pzha4QZ0Uw9JUCf3pKeFhx2JJy5G5X6o8H1/O4lKlUblcnYhJhhcILe6N4umQyzxpV5IIETFkw2r5MumNGCY9ADzOwPyvpS30a19Kifuk06F5UR97VF4yHXZkRXAWzEf2jGzrN+kMBpCBjR69Bzqgaz3DhGBdyZRqTqRT3P84dX+qlax2HAJg/INF9gvg6Y/T/UGlN2nkVI4DQEzHgRmtH0qadlxezxG1pS32ujeL/0fUyCdnX7xagcO2bBhMdsxnZJT8zYEWWgqLJwYLqcMgDF4ADG3eDma0i0pY5ib/XzQP4i3+2OKsBp52daNiqTsSn/Q7LgmH9GeAp/YCGBExZMeBFq3vaZPAbHVL1/k1HXh8zKZ9T3VRUDiWmyPDiU6I+QYfIHuuaYXLSTiyocUDyLOS04Rg0xNst/qBd6CIXqM576TDqbuCkDVHVC1/xFRt3jOqH1f6hnh8AASOCEBZPllAFgumPWOw8OWXAMoh7n4rZcoHlnfl1J4JjKRaXTNWGqZIc618tXbtD3mAQzdp4ZSOCEA7LgGGCxIAuOadGi15hxPtBmIBAdz793Vnvq09NFZUL3hlmOU4JRFhyNMcRCZsLwBx0hgRMWTDavE6BvfgWzWHAkDauOjZUJGxJN4TSYLyqpDb+MjuWXzlqF+jQ+z24TdjRMc5wM5KIyDpN3vEjghANTWnCoZyA5Tj17Y2f7MHG3Bsn4zr+TX0bH8UuX3VOdnsn4TJgkTnKtmSUGR890DSZsl1g0f7ZQDA4Rbsx4U5qxZ6Drw8CE50MvtGhUm6Tyyyg/FhzNA4H1zJKsE6YU0ya14JjlmmAxY/gDAw0TDwtmtOBojB6iTG9xZ1RApllEql6EasGprQIO/i7d1qgVv4y28UtnTd3qDgU9xZNuQcbMcZpmFJWOxyzpZ5zl9yFgzs4zAwmccGC2mbkBc7jV9G6sDevdma8h0RRJoxpE+a8mAX/niO+znxJjbwQLjkvJgqN1DI4JzflmtOCYNQbHEPQcJm6+a4VcVGFBT7FgFnQQZbrHE+gZv0EWHC+hWllYcQMAHS4S14UYHKcQg2PWYeI6QTE46nVr3ocxyTXBYsZrmoEEjlHoGaBqiBmRLDjknzeI+o6iiokX1wVLjktB4GguUg3KkqxpvdQ5klZt7ge6OZ8t+kECxygkF4cZ56LSGD0sFnr3Ro2yspiwIdEUt1NcD/VcWKKAxNbi+yghBkdwUelocnfrKRbMGINDif5MjykFsAjF4BiFUQ2JXhckxeAY17szeaNSb0J1UUXH8UHE2U8BXUcDcYnMPlmQsZ6dATP2ds1ynCy6Bhmb3IKjNWa8phlI4BiF6XM3mCDRn97CQFf3BjWsXkI5z44aUbwMvBWIS5LuNzLImIaJG4RBnQsTPtA1x+TtErmoDIMi/yXo0ZPWvbHWs/dPpnEvoVhwtn/FLy1RgC3Rd3+4gozNMtmmGR/oph1FZYDLjmJwJJDAMQozWnB0vXH0sODoGOcEkAXHKEIJ1j20VvyM0jUqDzLWcyi3rsPEjQgyNsl1Z5TAoUBxc14fDCRwjEJXv7FOlgU9g3ZNGYNj0obVbISSe8Nexi9HzlXe7xNkTCJVgq7JCXXCMBFiFpGqY8fOjLmdGEjgGIVRYkFTC45Bo0JMY8ExKEDVLA9HADixFSg9qm2docSy2Mv5ZUIz5f0+QcZGuahM8h/qOjeXXufApK5iU54Pk7ZLHijI2CjMGPlvlG/XLDE4RvUczfJwLDkMvD2UX3+qVLt6QznPggVHKf4GMDjI2IQzUZvSgkN5cCSYMfzBIMiCYxRkwZHVrfcoKj1uRqOGGJvkQVO0W596Q/kfBQuOrYnyfp8gYx1HOpnRnG/6B7qZrHAmdFGZ8fpgIIFjFHpaQ3R78Bp14+hQpx4PGLP2/vVCcP8AGgvrEMSvvYJfxjZS3h8tt+Aw9ZWfED+vBboOEycLjgh7fWg9Ws3kVjg96zaLaGcggWMUulpwdDLZmi4GR+fGmpLESRHcPwDgcmhXLxfCA6y2kl+qWXC8QcZKw8QBbPlYvW6XE9jwLrB3mf9jEDCjWDDqutOrXTKLlUVXTBZKYCCGCJz58+ejffv2iIuLQ2ZmJjZs2KBa9t1338XQoUPRtGlTNG3aFNnZ2T7lb7nlFlgsFslr1KhRev+M+mHGAFWzxQ3pfTPqOhGfCRsSa5S47tZS4AQpDjgOsHtifwJZcNQETs4M9fr/WQH89BCw6F98QsFAmNJFpaM1RO17NK3LTB0NsuAYje4C5/PPP8f06dPx5JNPYvPmzejbty9GjhyJoqIixfKrVq3CDTfcgJUrV2Lt2rXIyMjAiBEjcOzYMUm5UaNG4cSJE97XZ599pvdPqR9GuTd0a0g0Rherk96BunoOIWWrNklDYmEEjqYWnCCv5+oz4rqqwPHE4CjlwQlEJdNGVRcHLm9694aeD3SdXOfkKtY3BifUaVMiDN0Fzssvv4wpU6Zg0qRJ6NGjB9566y0kJCRgwYIFiuUXLVqEu+66C/369UO3bt3w3nvvwe12Izc3V1LOZrMhNTXV+2ratKneP6V+GNb4mcRFZXoLDjWssDLNRyjCIRDBnovKU+K6fIoGAcFFdepvhboDIAQwA0BViALHjL1/w+53DevSMwbHLB0NsuCooqvAqa2tRV5eHrKzs8UvtFqRnZ2NtWvXBlVHVVUVHA4HmjWT5rlYtWoVWrVqha5du+LOO+/E6dOnVeuw2+0oKyuTvAzHMAuOTvWaIW7IyCBjU5nGDUAvF5W/c+Gs5pdN0tTLePPjWPi66ixw1NsXL2YUqaaMwTGhu19XKAZHDV0FzqlTp+ByuZCSkiLZnpKSgoKCgqDqePTRR5Geni4RSaNGjcJHH32E3NxcPPfcc1i9ejUuu+wyuFzKvci5c+ciKSnJ+8rIyKj7j6orRj0c9boIzTDyS29hYFT+DbMIHPY49XJR+bs2hLgadjSXnITmYj2uWkhHOgXgn5Xi+p/zgDWv+i8vicEJ/mvCimECRy8LjnbV+tRnxvtQ87pNGFfGENGJ/p599lksXrwYq1atQlxcnHf7+PHjveu9e/dGnz590KlTJ6xatQrDhw/3qWfmzJmYPn26931ZWZnxIseUMTgmM33qbsGhnqME9ny4nRrWG6QLwuGx4ETHq5eJSZCWV7ouig8A+1cC6f35l0AtY8HZt5x/nXMZ0PIcleMOYYqJSMGw606vuuk+NM7yZpLzwaCrwGnRogWioqJQWFgo2V5YWIjU1FS/n33xxRfx7LPPYvny5ejTp4/fsh07dkSLFi2wb98+RYFjs9lgs/np5RmB2eJZ5PWaYc4X3eMJDOrhmqWnxB6zbgKnnhacqBjAYuXrdFQrXxfz+onrjxwQ3Vouhd/EuqqcdgAWcaSWKR+OZo/BMYm7X08MSyKrbdVGoKuLKjY2FgMGDJAECAsBw1lZWaqfe/755zFnzhzk5ORg4MCBAb/n6NGjOH36NNLS/Pjiw41RAYhm9HWbJZOx2QSf7ujlogryXAgxODF+LDgWi2jFcaoIHJay4+K6kGOHRRByTjvw2gDgjUzxt5tymLhB17QZ2yUz3od0PiToPopq+vTpePfdd/Hhhx9i165duPPOO1FZWYlJkyYBACZOnIiZM2d6yz/33HN44oknsGDBArRv3x4FBQUoKChARQWfcbSiogIPP/ww1q1bh4MHDyI3NxdXX301OnfujJEjR+r9c7TBLNk3DRsVYhYXFQUZS5BYcMIQZCzkpvFnwWH3O2tFETLqWeWywu/gOKD0sMJ+j8A5cxAoPQIU7wcqChWO1ST/oZ5ZgdW+R8u6zNJZ1BM9YwPNKNoZdI/BGTduHE6ePIlZs2ahoKAA/fr1Q05Ojjfw+PDhw7Ayw03ffPNN1NbW4l//+peknieffBJPPfUUoqKisHXrVnz44YcoKSlBeno6RowYgTlz5oTfDeUPo4aJa9pImc2Co/MDhib5kyIROGEYJi7MEO4vBgeQTrgp1N28s3JZQTSdOaBybJ7fyQ5Rrz4DJLWBKQMydb1n9LLgmHzAhtbQ+VDFkCDjadOmYdq0aYr7Vq1aJXl/8OBBv3XFx8fjl19+0ejIDMTsQca6mq81EmWmtuDo2JN21ACn9wEpPbUdDWdE/iW/MTgeMRITp14G4ONwAN6VVOURJmqzj9d65qaqreKXFivQshtQtJN/f2ovkNYfqDwpfkZIOGjGh4GuOWX0uj5oNKMUOh9q0FxURmH6RH8m6Bno/oAxaUPyyVjgrQuArV9oW68R+Zf8PXS9FpxAAsdjwTmRL25LaKZYFDWeqR+EEVpJbYB2F4j7c2YAr/QWhRIgChwzmvNNmQfHZO2S3tD5UIUEjlGY0oKjp39eB7FgaguOjnUf+oNfbnpf23r1cmEGey4cIQqcPT+L2yxWoMNFvmW/ngysf5sJYE7wzZLsqASObxHfb3jX97jNOEzcLKOozNrR0AuyaKlCAscozPhwNEqU6ZLoz2SjqIwIbtQyTgYIv2s0aAuO4KKyi9ssFmDYDP6zFz0K9LxW3PfzI1LxpDQNxJZPxPWDv3uO1YTz9ujaQ9fpHJjRGq4nZkxBYhARneivQWFKC45RMSd6WHB0uOnNKFJZtMxVAxgjrP2xfxW/DBiDIwQZMyO9rNFAu8HAjCN8HpvFE6SfObWHX545AMSpxOvIcZuwt2uYBccklmU9rSG6QRYcNciCYxSG9ZT06jXp2VPSqk6db0Y9H2BGNCRmfBj4q9etkH9GCUHgOBkLjjDySkjS1/1K6Wd+fZxf1pQCsY3919/Jk1zUjL1dXac90KtdotGMEvQ8ZppNnAgKM/pJjRJlZkz0p+uweZ160roKHL0y1fqpV0jE1+VS//UJAoedPFOeHLD39eqft0b5r19wTZlymLgZLTiUj0qCGZ8tBkECxyjMmH1TV1OwHsess0XB7L5uPWNwwhFkLEyb0Nj/tC+iwCkTt8kFjlXWFAoWnV5jETD9vRCvY1Tv34xiQdPrg13X85hNghmfLQZBAscoTJkS3SDztS4WHB3Qs4eu57n21quxwDEikZtavS6nOKTbO2O4CkKQsVAewIq/T4Hzd8yCKGpzHj/iiqXLSKBVD2D4k/x7IdjZqGHimlrL9Oz9G3B9mNHdrzlkwVGDBI5RmDHy32y9O0OHiWtdtwENa0Oy4Ai5Z2AB4pP91+eNwanxbrp14SYs31UkLde0vbjOjqKSJ0e88lXgrrVAm4HSeimnjHrdNExc9j1m+Q8pBocIBsNGUWlaMbNqgobEyBgcM2R29vkOkwQZB3OeBfdUfHLgGBlB4MhYt/+0dMP1H/PLRi2lE3k27yKWeXg/kOiZ1FcYni4kBTTsYaCXG9oEnRjAQOuYWQSOCa1wBkHDxI3CjPEbZjMF6x0wqeeIAiN6jia04DjdbuVGShA4gdxTgOiikhETJevfCXE5jmpxqoboOKBVN+CGz4EmqUAj5vsEgeO0+/5npnFRmTBg14wpN3y/SMOqTGZpNxASOEZhxptST8GgtwXHbMPQzdiQGHDdlVbZoShhqov5ZVACR9mCE221KJerrQCOrJPu6zrKtwJBEDmrFcSjWXr/BiWv1GuYuFnc/X6/p96V6VQvDBR8+kAuKqMwmzUEMM70aZpMxgaNVtDL2qfhPJs8+pwPjsk3JNcgXrwuKpU5pVhiGylutsortzXxLVS0S73eaBu/dNTAJ4Cbev8mteAY5ZLRy2Wn8TFTHhwiOEyoss3W+OluwTFqFJVeDYnGCkenY3YyjapV7boLxUUV3zS4L1aakqHHVerlhWSBLruPBcfp1kmkAvq5qEyRCgLGdTT0xDSCz4DRnTpCAscozNiQ6JpXxowWHBNa4Vjko4Hqi07nw+0KJshYcFEFIV4ES4sMu0NmdVEKVk7pqV4vO0WEo0pWt8bTYrCY5uFoRBC6CVznyl+kU13kOmchgWMUZrOG+NRlgsZP96kazG7B0RidHmAcc57dAQVOEBacZp0UN9fIBY6cblf4389O8skmEQTgdGkY0O1zDsxipWWvDxMOE9c1BkcvK5zW58OE2bkZSOAYhdniWQCde0omHEVlypwhLFpbcHSKwQnmXITiomrcSvJ2uP0FAECNQ6HuGCZeZ/TL/uuNigEsHqtPjVTguFxaPsDkI7TMkujPhC4q3ZJXyv9Dk5wPo4KudYIEjlGYsafE1OvUssEG9I/B0dlFpf35MKGLSvIw0LBaJn6FU4tlCSnIWJws83j6SPzDtQYA1DgVrCx3r+OzF1+3EGiSErhuYSSVzILj1jIGR35PmyXOLpyZrrWoWzdLmY51k2VZAgkco9DxQuH0MiMyx1xcafdTMHTKq8WssqXVDm0qZY63vEajOiX1i+f5RGm1tlWb0RSs08OAteA0q/gbcNb6FgplmHhSG6BRKyC+KX7vPsu7WdFFldwWuG050POa4A5WiO9hJ/IE4HLTw1E/S6R4nH6n26gDVXax3eD0spQpva8PjEtX0+sOMMiyrB8kcIyCuTgq7No+fEur2AeAPo2f1hYLB9N7rqnVKCCTOd5T5TV+Cta1fv0sWrr9h3piyOg9ABvf9S0TiosqNgG4dwtw/zaccopxM4ouqlARRlJVl0g2uzQdRaWne0O/h1ZFDXNN6+SSKSzTtqPhcIptUZVW7RKgs0gV21Ktz4fbzZ4Dk7RLDCRwjIK5wE+WafvwrXWIgknbB694QVs0vrjdLrbXodEx693DYOrXs+dIMTiyuk7ukb6XTLTp66KqcbjwzeajaD9jKQbMWYbf954EbI0BWxOUMZa9gEHGwSBYcL67S3qIJnRR2Z3ajvySWlH1EThlWll/vVWL56OmVsvM38ZYcMqqFayd9aCS+Q9rlVy6EQ5lMjYK5oJWHRlSR6xMbEWl3QmFbB51g7nZtVbCLkbgaCbKmPOqmj+lHrhcTngHEmv8H0ZZxPpcbg4BZleqG3oOE9fTqiBPold2VFyPS/auvr5iL+bl7kMtcz2drqzFXZ9sxsbHsxEXEyV5INY4tbDgxCludms6isoYF9WR01XorF3NsOjm/hLritL4kna7WSutjv+hpu4v8Tg1j7JjhLrd4YRyTvDIhQSOUbBiwaLtw5GT+GD1GZ5q0fiYWdOnVr1djnN7b3CtzzEA1DqciBe/TdvKmevD4XLpI3C0Rq8YHPn1IL+mK5hZwKOikbP9BO74ZLNqfeV2J/785xQu6ZYiiffyyYNTF6KVm3yzBBm7OY7pvGhrOZRafbU7ZrajobVlmW1LNbWG6znU380KHG3Phy4dUQMhF5VBsA2e1iedrduhpRlRYsHR78bRSpQ5XPr1ZADZudVxqL9DtyRxeo6iqsf5qCgC/nzdm9uGk1tsZEn04PS4eFt2x49bjyuKm4dGnIOVDw3DtefyI6byj/AuLVbgnKrQIHD+9D+Km/V1UWlXt71WPB9aXx0WVjBpecxO/R7orLjWNBu1jiKVc+vX7rlcYlvk0MLiaTAkcAyihomT0doaolevw6FjQyKJwXFpUzfbWOvhonI42REW+v2HDi1N4ywau6iKK8RYMnd9HmBf3gL8+h9gyR0AFCw4LlmchUdUcJwb0z7dItn1n8u7Y98zl2HaJV3QoUUj9G2TDABYvOEwALnAqcX8lfvgcLlRXuOQPDgBYPuxUvT9v18x4b11OHqmCgdOVfoeu4o4d2s5mkXHiTxrGDGtqdUJgFUSs6Zd3bXMMXNaCxzmODXNZaRjgK6TESFWja1wEguOlt4BgyAXlUHYa51I8Kxr7T1hY3ocGt6UdqcTMZ51rXsGbolbTZtjttc6IUyXqLUAAaSWFU2HkEL6UK/VqafEwaLp/1hhd0AI8S2pqkUQGWmUObSGX+79hV/Kz22tTFj8eD8AwHJKDD7On3UpkhN83UUD2vFTORSV21Fe48D2Y9J8NS/8sgcv/CLW88XtWejTJgkxUVZc8dofAIA1+05jyHMrAQCv/7s/UhLj0D0tEePfWYsfHQqiB1q7qPQLULUzHS+XxsKateA4XG7N4jck94fGosyMFpzaWofYTmvcULPXhBktOCRwDIK14Gjf+2duSg1dVDW1Togp0vSMG9JI4Dj0HdLIDiHV+j/Uzc3IcKY+IkQBF3OcpXWtW+m/l5/bA6v9VjGwXVNFcQMAvVqLIffv/LY/4OFc//Za/GtAG3RooTwDudxidCA2BR2shT7lzOKiYq0hWk8Qylpw7A6XdgKHtTppnlNMJwuOjjE4tQ4HhKvVonm7pFN8p0GQi8og2FwvnMZmRDZmQUsXlUQwaGyxYB/oWjUkNQ79BAgg7e1qXj/7H+okcMpqtI3tqWUEX52vu90/+m4L8Vq78fx2fvcP78ZP1fDain3ebTtnj8ScMb0QG2X1WnkEvso76rXqtGxiw01+6n/SeQvKuASf7Zo+eGUxSVqKJ3utBv+hKoxlWcNrmh2urHlnUTeB45a91e586NkuucmCQwSDVCyYIzBOOspE62Nmff/a3Ox6CjJA+jDQ0wqnV74JN6et/Zp1FdQ5buiLm7yrXGof3oUWwn83vFsrjOnf2m+Z/m2Tkbu7SLItITYaN53fTiJeft1RgKkf50nKvTtxIHq3TsKxkmqs2F2EG89vi6yOLfDz9hMYd14GbnofuNT+PNbHTZN8TqtrGuAfhuw/Z3e44Cup6gab+0bTuCFIh4lreU1LBY7G9zmnT1vKcZz0P3S6oJxgIHT0dJ2z17HWFj4jIIFjEHo+HKGXBYcdYaHxMbO9o2B6pHanC8fOVKNjy8aqZdiEh3pMd1Crp5uRfRjoFGSstX+eddk5NejdHayOR5uaSiSiwmdfwclTOPLZ/WiefR86MtufuqpnwHpvymqPBWsOoriST4KWc/9QxXIjeqZi/r/PxfQv8mF3uvHidX3RLyMZALDglvMkZUf3SQMAtG2WgDPFVfKqNBULVbVOsA6zGodDM4FTy3RitA4y1kvgOPS01OpkwampZVNM8OdDK4FTWS2OBtTTda6XZVlPSOAYBOs3tmjd62AvQg0fjvJRJf5wuTk88tVWrP67CPP/fS4yO/pPoe8KYf6UT9cfxmNLtqIRavDaLUNxSTd+EkSny40DpyrRJYUPLbaH2PAdPVOF5IRYNLYFdxuEWn9IuPV3UWkdKO4IMX5jx/FSHDxVhct7p8KioLaKzpTiz6dvwgSFv6Pp612RanECX/zg3VbJ2ZDRLPCjPik+BpufuDRgOYAXLoJ4CYYx/Vtjfm65z/YjxZXo7nAhLqb+GY3Kq+1SgaNhhl1WtGsucBhXfK2GqQ/sLh1dVGyiPw3PR1WtQypwtMjB5KGihhE4uuYFMp/AMSQGZ/78+Wjfvj3i4uKQmZmJDRs2+C3/5Zdfolu3boiLi0Pv3r3x008/SfZzHIdZs2YhLS0N8fHxyM7Oxt69e/X8CfXGrmOAKtv719aCE7xbrdNjP+HrzUdxqqIW497hh9UK1DhcuO3DTbjhnXU4Wc7fjJxkmLj6jbPzeBkeW7INT0R/gh1xk/HxV99gb2E5th8rRef//IxL//cbvtx0hD/eEFxqr+XuxZDnVqLv//2KM5XBpTevNajnqF9KdG0ljsMVfOP3w1/HMXreH7j7083oMPMnnCitxqNfbZWUybTuxoToXMXP2yy+D8iSCb/W4ai15YHsLvhjpq94soDzjsKSc+h0JdrPWIr2M5bins+2+Fh7nC43VuwuxGlPnp7yKmm+nloNp1Rgr2ktJ2p0uzmf5JVa4QjBFS2c6wFzlmHL4TNB1B68BefAqUo8n7Mbfxf6Clw58vn2AiWZPF5SjYLS4Kb0qazWac4vhOY6r7A7sWpPkWQ6lHCju8D5/PPPMX36dDz55JPYvHkz+vbti5EjR6KoqEix/J9//okbbrgBkydPxpYtWzBmzBiMGTMG27dv95Z5/vnnMW/ePLz11ltYv349GjVqhJEjR6KmRocJFjVCIhYCPHydLjeeWboTt3ywIeCEbxwnbUgCPWjmr9yH9jOW4oJnV/gMKec4DodPi8Kk0i7eOBY/gdFKM2sPeW4l2s9Yiu5P5KDbEzlYvqsQa/efxuPfbgMg9e36M+dPXLAeADA5+mcAwCT7Ilz6v98kD4+HPQ/KMvZB4OdGd7jceGnZ3wD4Rr3/nGXYcbzUp9zpCjt2nRCHFeslcDiOk4hULR8GknOrsY+KDRz1F4BYaXfins+ko4+y5q7A55uOYJP7nDp9NwcLWp/Tr06f1RKLxYK0pHjf7QD2FVXgryMlcLjckuvlleViZ+yHv46j42M/4ViJeA8t/PMgbl24CY8t4e+V8mqpwLFracFx6uOiKq9xIkpiwfFf95nKWmw7Wqqa5oK9jqtDcPdf/OIqAPyUHde88ScKZfMA/rztBHK2F3jfs654fxacI8VVuPjFVXhj1T8Y8b/fvGJUjWq5wPEjUg+cqsTgZ1fg/Lm5+HVHgc9+juNwnLleKu36uajYASz+AsU5jkPmM8txywcbkf3Sas3jueqK7gLn5ZdfxpQpUzBp0iT06NEDb731FhISErBgwQLF8q+++ipGjRqFhx9+GN27d8ecOXNw7rnn4vXXXwfAn8hXXnkFjz/+OK6++mr06dMHH330EY4fP45vv/1W758TNNuOluKJb7djXxEfT1AbggXn8nm/493fD2DVnpP497vrJeVdbg6Xvrwa/Wf/ikq7EzUOt/Sm9NPrOHCq0js65FhJNT7zJD8TeDZnNy58YSUWrjkAQDrRmgWc4s0GABPeW+9df25sb8m+allPZdPBM6iqdUoyZKrF4KzcU4RTFbXoYhHnHrJ7Mz5I2X6sFIdOibEb/s7x+HfW+WwbPe8PtJ+xFH/+cwouN4dKuxMDnl6Oy179He1nLAXHcdKes4ZuxmqHS5KgS4t4FoFKtmHVUeD4G0L6x75Tqvuio+rmwrH0ub5On9ONW3+RvD3PuhsAcPX8Nejyn5/RYeZPWLPvFE5V2LFkyzGfj1/w7ArUOt34Ou8onl66CwDwy45ClFY5UC6bQLEmgAXn4KlK3PbhRvxz0jeWSQ4bR1WvZI0ySqprJck2a13qx1xV60T2y6tx5et/4LYPN/nsn79yH3o/9QvW/sPPIM+2S/7u8883Hob8OZv531xvG7lsZyHuXLQZd3ySh7X/nEaNwyXpyK37R/26nbRwo+T9gKeXSzpDAC+CBKuY/D/056JavFFsl6d+nIfv8qXXy8xvtmHwsysw8xu+Y1ep06ztLjcnCX/wZ8FZt78YlR7hXVRux3Vvr8Xqv0/qMpo1FHQVOLW1tcjLy0N2drb4hVYrsrOzsXbtWsXPrF27VlIeAEaOHOktf+DAARQUFEjKJCUlITMzU7VOu92OsrIyyUtPHC43rnz9D3y87hCyX14Nl5uT+qD9/OlVtU78XSg2TPlHSrxJxgD+4t9bVIEzVQ58s/kozlTVwmoJ7KKqcbi8vRmBWd/tQPsZS5F3qBg/bTuBt1fzeUKe+mEn9hWVI2f7CW9ZC+AzwoTjONy6cCP2n+STnT14YSrG2b/G44N94yJuv6gjYqIsOF1Zix6zfpE0frsLyrwzO3McB7vThS82HcGkD/hG5C3ba96y5/Xrj9F90pDdPQX5sy7FOSl80PEVr/2B4kqxd2bh3NjLmI7tThfyDp1Bv9m/Iu8Qb6q+P7sL3phwruQ4//3uenR67Cf0fFL6wPp43SFJ0HUgX/faf07jvGeW47q3/kRRgNnjS6sdEoGjpYuq0q6f35wVOP5E2fTP8wEAEzLb4sDcyzH32t64ZXB7LJ9+Ifql+Vo/vIx5S31fo5ahHq6+tD0feGAn0J4PYE6xlPgUmfDeegx8ern3/dJ7h0j2n/P4z3jwy78k2575aSd+2yPNs+PPvbHjeCmGvbgKy3cVYfhLqzF/5T7J/r2F5Xhl+d9ey7DDEZwlFeAf2De9vx6Pf7sNFXapYKl1urH58BnvfSy/pv1Z+J7P2YPTHjfx6r9Pov2Mpd6ZyDmOwwu/7EFlrQsfrT0IgE8wKeDvAfrxukMA4JPTqPN/fsaY+Wsw5SNRTN3wLu8+Z9ulSrvT53e63RwKSmu8HVeWy179Hd//dRwcx+GOj/Mw9PmVmPPjTgBAQZk0EF3NguNyc3jv9wOSbfctzve6NNvPWIrFG3mX/JebjoLjOFQzlvZAguKrvKNeC77cmgUAK3cX4ekfd4LjOJTXSP9Df53nG96VdhrzDp3BzQs24H7PvR8udA0yPnXqFFwuF1JSUiTbU1JSsHv3bsXPFBQUKJYvKCjw7he2qZWRM3fuXPzf//1fnX5DKOQdKsYXG4/ic09MiMCafadQXSu9KTmOUwy0vJcx5XdLbYLdBeU4VlKN9jOWok+bJGw9KrpSlu8qQq/WSUhlbspfdxbgEjcHq1Va98I/D3rX78/uIjGTj33TVxhmv/wbrrSyFzT/HdW1LnSfleNTvlUTG6adeAzYsA63JbfDbc9u9fmNRWVi75U1X1vA4YZ316FFYxuW7fRNmtYJ4vlMjo/G/LGiKLn74s64b3E+AOn0DBYLh0v/9xs+nZKJf78rWpgEBndqjvuzeffI3Gt7Y+Y323zKsMz6bgeejK7x3jEOp1v1PzxVYffe8CfL7Rj031x8MjkTQ7q0AMA3Yv1m/4ryGic2P3EpSqsdSGaOfeXuAozwezT+qWGCW9kGWmursVOSxl258Vu1p8jbsxvWtRUsFgtuGNRWLODyE/8Ul6i+L9l/7puwkNQaOHPI+/bg3Mvxwq97MH+l73xV/85si57pSch7PBsDGNEj54tNR9HFUgTYxG3LdxzHoIG+ZQvLajB6njTu54Vf9uCqvunIaJYAh8uNS//3GwDe4vnezedJLCv+XFSnKuwY+jzf0fp9L/DJusPY+J9stGzCH9j//bADi9bzloe1My9BSZVDasFREe21TrekbRLo/dSvmDK0A4rKRffLzx5XUhljDXG63Fi3/zTO9wxqEFIXvLzsb2/W6tdu6I/OrRqj2xNiu5V/pMTnO8e9vRbfsW0IOFz+6u/omtpEsV0CgP3/vRxPfLfd+9vv/WyLpA1f+OdBZHdPQcEZqRv/y01H0LdXH3Ach1MVtdh4sBhf5x2VpDNY/9hwZP5XOSYNAJxuDvNy9+FMYSm8hm0/AofjODzkEdDHSqqR+d9cTLqgPZ68kh+JmLO9AHd8wndiV+wpwjs3DZT8h7m7CjAK/Dn/fe8pZHVqjldz9+LNVeL1fdP57bzCEgC+yz+OV8f3Vz0mvTkrRlHNnDkT06dP974vKytDRkaG5t+zp6DCR9wAwMQFGzApSrwILeDwwOf5eGV8f6/idrk53LVoM5bv4i/wCZltcffFnTH42RXeelhxA/C9ndV/n8R6m9gwWcFhyZZjaNY4Fv/5Zhv+NaANPlp3CCVVvMAa1rUl7hveBUO7tMTYN//0OdbkhBhvWfbRLawriRsA+GZCBiwLPSq+hL/A5Q//GZd1w4nSaqzbXywVIwC2HC5RrDcl0Qawz0C7NKDvij7pOHqmGj9vPwFrge/NrSRuAOB/4/p5128Y1BZX9ElDabUDLjeH+z/PR1m1A1f0ScfgTs0xzuPSssgav/8t34vpl/rGkNwrizcBgBvfX4+Xr++Ly3unSRraf735J/pmJGMmU3eV3Ymishq0Sgx9IOn3fx3HvZ9twYTMtnjmmt6o1FHgsA8ttd6dYBVs3igWl/ZI4Rtg9rpw+oldkM9BJdAkDeg/IeTjNQQrYxTf8Q0eHjkWD43oivUHir2u0Vsv6IBZV/YAADRvbMPWp0bgy01HsfVoCfKPlMDp4vDZlPNx/dtrUVBW4zO/UO7uIlx1rFSSpRmQdmLaNI3HUc9D9eYFG9CiiQ0bDhR79y/fVQSHyy2JDazwExzKPsQEzntmOV6+vi8a26K9D3gA+PDPQ1i67ThWMcc9b/nfGDZ8NAB4OwYnSquRNVds3zb+JxvnPSOKvXdllgwAaD9jKa6LKvO2pVa4Mf6ddXh1fD9vR4cls0Mz73na/MSluOPjPGw4WCwp0yQuGuU1ThwvrYHVJr3HDxdX4bBCGgAAeHpML1itFjxzTW90S0vEE99uVyx34/vr0RonMYm5nVftKULmf5ejsEz5+u/TJgkpiXH4ftoFuOr1NZJ9QscXAP63/G9MjhLPs5tzo9LuRCOFkaFfKDybPlhzEB+sOYhxAzMkz679JyuR/fJqrIqVno+c7QV45qedOFLsG3cJALOv7omhXVp4rf2fTM5ULGcUugqcFi1aICoqCoWFUvVbWFiI1NRUxc+kpqb6LS8sCwsLkZaWJinTr18/xTptNhtsNpviPi3pmZ6I8edl4ExVLa4bkIEjZ6rwfz/wJkq5WPg2/zhydxehXCW77P9d1RPRUVZ8fedgfLT2IH7cesLrz/35vqG457MtXjOpVfbgZc3c81ZITdQLbj4PFosFA9o1xd5nLsMPfx3H9C/48l/ekYWUJnG48IWVnrqkVhYlsrunYPbVPZF+ZGnA85OSGIfFU7P4N/PnACeV6z63bTKGd09BQWkN7svuArzI7LRL3YtRVgvuvrgz7r64M/D7RiBX/XjH9EvH+EFt0bFlI7RqIhUPTeJi0CSObzWX3HWBZN/30y7AZxuOwLqZrZPDvNy96NsmCRsOFiOzQzMcPFWF/acq8KcnVqBTy0ZwuDhvAzn9i7+851pg/6lK7D9Vicds7LkGlu0qxITM0K0Uz+fwltFF6w/jwRFddRU4Tj+jqDiO712u3c+fi6/uHAysnAts/giYkgskpvMFXXUQOBf/B4hVnkoh7FiYmKLNHwG9xsJiseD8js1x8NnRih9JjIvB5CEdfLa/Or4fHvzyL0SVSAWOfITW0nuHYOOBYq8ImXlZN9x+USc89f0OLPzzoPcak9PlPz/jAcYqWet0YsXuQm8aBoEjxVV4/w9ebIzunYYoqwXf/3UcAHyuZwB4a/U/ADhExUnbpW1HS3HHJ3mSgGqBjGbxaNnEhj8evRiv5e7D4eIq77XTMz0RNQ4X/jnp+xuEdlVJ3ADAh7cO8q43axSLL+7IQnFlLRwuN2KirGjWKBZbDp/BxAUbUF7j9Ol4KdGqiQ3v33weercRBaaQNPLjdYfw9I870allY9w2tIP3/MgnWLaAUxU3SfEx+NbTBvVpk6x43fy+9yRuep8fjWyFtO2Y9MFGfHFHlqT86Qo7Hv2at1LHx0Th3uFd8FyO6EVR6pgDUku7FZzXwqPEF7dnwWKxYETPVNVr3Wh0FTixsbEYMGAAcnNzMWbMGAC8GTQ3NxfTpk1T/ExWVhZyc3Nx//33e7ctW7YMWVn8H9ahQwekpqYiNzfXK2jKysqwfv163HnnnXr+nID0zUhGX09iMIAf6icKHF+xoCZuds0ehegovic4oF1TDGjXFK+O74+9heWwO93onpaIx0d3x/Qv/kJxZa3PBa7GH49eLHFdxURZce25bTC0S0s0bxTr3ffBLeeh3O7EsJpigB+8JLnxHx7ZlRcULPm+PTy/MAGNE7Pa4pJBF6F1crxy3hBbEmD3WK9qGCvW4fXApgXApbOBJimSOls0isEzl/dC/4ymaNE41tca4nIAPz0MdLgQ6HWt30Pt0yYZfdokAzFtAM/9LZzFyZ6gSMFSwbLk7gvQKDYaA55e5rWKqSFtSNz46M9DIQuc4yXV3l47AHy24TDOSWnifa91HlLJPEaMBedkuR2Xvfo7TjEjSzq0aASsfpZ/s+pZ4Kp5fABjiTTQXUIsE8v1VCnw+Y3Asc1Aj6s1+w2aY2EsOKW+wcShkNmxOf549BKU7W8CfCRut8rEO+uWirZaMO483jr91FU90S8jGYs3Hsa6/aLVYlTPVOR4BgxI6+Jw68JNyH3wIkx8fwOOlVRjUIdmEsvP7Kt7Ijkh1itwWOZe2xvPLN2FCrvTp4NhAYcrX1ceNn9e+6ZYdNv5AIA2TRPw3L/6AOAHRewpKMewri1RVevCuXOW+RyzUkfmhkH877+8d5pie9KskXRWrP5tm2LbUyORd+gMGn9iBTy36uQh7dE4ugsu752Kbql+3KUMbHZsjuOQf6QEH6095POfse97pCVixmXdsGrPSaQk2nD7RZ0Cfs/QLi2x6qFhuO7ttYiqkj5bNhwsxku/7sHWo6X4u7AcV/RJk1jDfrpvKDq0aIShXVrgleV7sWJ3obfz89eTIzDru+34Lp//f1lhptbJfWVcP5yT0gQ90oM7R0aiu4tq+vTpuPnmmzFw4EAMGjQIr7zyCiorKzFp0iQAwMSJE9G6dWvMnTsXAHDffffhoosuwksvvYTRo0dj8eLF2LRpE9555x0AvNvj/vvvx9NPP40uXbqgQ4cOeOKJJ5Cenu4VUZFCenI88mddCovFgqTN/wD8/emj5gHg8dHd0SWlCXqlJyI+VnlkSRfmYTWsaytsfuJS1DhciH0pGvDEi92U2RatErogPSke3dMSUety4dy2TRVjRQQEP7rAxZ65e7BFbKzjY6xYfe8wNGsU67V0SDjiOzLJL4wYsUVZ0UktQ7HLKYobQGrBWeCJVOFcwNj3wA6/t0VZ/QuEvxYDeR/wrwACR+mY1W52geXTL0Ki5zzlzxqBtf+cxsNf/YV2zRMw/dJzMKBdM/yyowAL/jiAo2eqEVdr8SoQiwXYU1iO9jOWYvXDw9CueXDWCnmcwOZDZyT/rdYWnBqHNAaH4zhc9foabDsmdaV+PvV8oIpxC3hcmChgev+JrYEyRhD0GQek9JJ+4fUf8/+Btf7J83Sj17XA6uf49dMqubncbmDnt0DrAUDTwCI20Sb9vf6uvXcnDpRMPDqmf2uM6d8aHMfh6JlqtGkaj9/2nmIEjm/naPhL4uSmrLh5/+aBaN6Yv54OPjsa246WonnjWGw/VoruaYnIaJaA9OR43P7xJknwMlu3nDsu6oQZl3VT3NehRSNvgHBcTBR+f+Ri7DpRhvI1WwCPvkpPjgOKgC6tGuOKPukYek4LnNu2qWJ9gRjQrqlk2E3rpDhMH1y3NAYA/6yafXUvzL66F3D6H0AcK4HXb+iHNp16oykjti48J7TA+fYtGmHjf7KB3/IBj6dPuDbYeddYcTOkcwvvOe3VOgnv3cwHc+UfKUFqYhyS4mPwwr/64p5LuuBwcSWafhvlfbZ0aJ6AxqXRuLJvGtKT4uF0c3hAwUUfSegucMaNG4eTJ09i1qxZKCgoQL9+/ZCTk+MNEj58+DCsjN968ODB+PTTT/H444/jscceQ5cuXfDtt9+iVy+xsXvkkUdQWVmJqVOnoqSkBEOGDEFOTg7i4rRKfq0d3saGeTi2bRqP3ydejOgo5RwaocD3UMS6u6c1RvfzNLromBwIFnD+H7T/rJC+d7ul8Qj2Cv7JLbgW2AkE/UX+b3xX+r5GYQTcqb2+9QQa8lrm2wMNCHPMfVon4utRWfjhrxOwO13IaJYAjuN7nbcN7YDOraSCLasT3xtnGdkzFSN7ely1c62Ax+DRPbUx4HnWX/TCqqBFzo9b+d809tw2+HozH7BY63JDGFCt5VxUbjfHT/LnefZu2H8Ks2bloIbJd3Lf8C6455LOvDXyNGPhq/QMv63wBFRGx/FBw6zAsVj5h//tvwMJnqzYFovUBRSJ9GQEjhrbvgCW3M6vP3Y8sLtNdi3/ct9QFDXqjMS4GKzacxJLt53AqJ6p6JraGJ1bNVGswmKxeLM+X3ROS3w+9XyU1ziRuf9PwDOYyJ9wurx3KoZ3l7quBBdNerLYhl10TkvsnnMZH1v1tFj2hkEZmDlgMFo2tqFN03i/HS41Mpol8L+hKs0rcJrGxwR2h7jdQHUx0KhF4C/hpBYtzZC1cb3TkwCZJanudYvXR3JCjDRmUcbHkwcpbu/HeB5io63o3Kox34bFWL0CZ+rQ9ph63kgtjtgwDAkynjZtmqpLatWqVT7brrvuOlx33XWq9VksFsyePRuzZ8/W6hD1h22kOASVYj74ujnl9XrXyx5ziPXWlAAJzfh1lxOYnwm4ncAD24GoGFmj7afu3bLYHruCwImO8z3eQI3T/lXiekUR0LiV//KAzzkY0K4ZBrRrFvhzwcCIp1svaI9d+3iRAgCzf9iJ92VzIck5XlKNjQfPwGrhXYh7i8qx9Wgpft97CsKkN24/o/dCpbzGKRmSagEnETePjOqKu4YxbsztX4vrhdt5902lJwir/RDxPxSI9QjEtD71PlZDsQbRpB74XVx/qRvwyAEgys/n5GKdc3tjyEb1SsWoXsrxjP7wTqVyVOyEXNK1JV7t1Q/fbjmGYyXVeHx0Dwzu1BzrDxSHbhWRHfO1/dOBOlpW/NYdTLv0432e2K8VvNVMy7qDRl6XhnUzOahaNY7F/kcux/5TlWiaEIMvNh3FwVOVsFr5Each3/vuIDuiEcpZMYoqIgjl4VuvujVEkrwtxGOuPCUKnLJjQJknWd+hNUDHYZIEUn5vHPlvqynzLS/EPYTSOCW1EddP7glS4OjV+EnrtoLDS9f3RUJsFD5edwi5u4tQXetSdV0CfFJEgI8nSE2Kw7zx/TFMlvcIABwuDrHRdRM41bUuuDgOjW3RWH/gtE9cAcD3IDf+JxsxUbIUWzVStxV2/QA4PfFCjVr6CpxhM+p0jGGHtVqqWZsk00qXAdVngMZ+3BM+97c+nZikuGhc3a81ru4nnZ39gs5BWD7kyBM/anm/hNoubfYEMH15C3C//3QQurXTPiJVy7qlIsRqtXgtyHcOCxzP479uHds8AzBkLioC+llZAP1UtuTi9iOilL6ziskCWs7kJ/K6k4JsSDJkwwzdDsBZIx1eLNzgoTRODmboZ/UZ/2W9310PwRcIhYbkoZFdvZvuXKQ+egEAvs7jBeTFXfkHZfsWjXBNf+mDioPFJ7N0sDhcbgx9fgV6PfkLns/Zjakf50niNxrFWnFB5+b49YELfcUN4Gt5qykVXVWNWkgT993+e3DuhEiEteCoWZ9kqQ4UrZIsuj4c9XLJGCPKAp6Lol3iekwQVvNQOotFu4GlDwEFAUQToHCcYRR8oaBnx9wASOAYhWEWHJ1uHH8NiZsZDdbCE/9TyQicCkbg1HqGegbbSMV4/Pt9bxAtNdUlvAtMQBhOzA4rDtTwnWQSTZ7xzbehiMzNqCkK/2FSfAyaxPEPzFV7TmLr0RLFj+48XobNnjxC3gBxAC9e1xepTJAxB+DyV39HXdh08AxOVfDO/Tc8w5FZQ8RTV/TAotvO9xl+70Xpob7Pk/OkUUupoJFbc8yExGqjYimTD3+XW7c4jndjCdt9rCEaWmz16qEbZbEIdCO+cb64HqsykEFSdwjn44f7+BjBJUGM3lVwM2pGsPGMkVa3AZDAMQwdLTiGNFJ+6nUyKb8F188RJsEe+3ATLCfBNlIOjxsjLhmI97i8dv8ILGJitIRsuJIHRYDzcJrJD7R/tXo5Fj1FqoqYXDtzuHf9qtfXKKZiF+akAfghpwJRVgvWzZQGNivlIAmG/yzx7aXKhxj7Rd7LLdgmisxGrWQCR6Pgy3DAxjhYVJpXuYCTW3DyFgIfXgF8MZF/b5Q1RFeXjIYP9LparGOCGdARwjUtjBwtDMKCI68rYAfsb2lgvj90teCEMHCjqhjY+L44eCACIIFjFMG6e+pbt6aNVJANCesuauJJ4Lb2dd7SAkgFTqgWHEE8xcSJMT0/PQQUiA91r2hi0/6H4lJz+89RI35Oz56S8vXR2BaNd24SAyPfkuXbOVZSjb88Ga4/unWQbxCh5Dzw+1whjhf/c98pb6K4jp4hpl1TmuC2IcwQZ7//Ya1UUALAAUZUdr1M6qIyswWHPQ9qAZ2tekjfyy04f/yPXwqB8D5ioc5H54tRFpxIEGXy8xyo7nCdj5oy4M0s4I0swOF/HjufuvVslwKd6/mDgKXTgRe7aHsM9YAEjlEY5aIKRzCfYGWJsklHgxR7eiA5TMCoksDxW7dHvETH81YcxTKeRoB1lQUryABpjJA/dBOSnKw+ad2X9hCH5z6Xs9s76d7K3UWY7JnVuHmjWAztohS3wjFr/AP3nd98kxIKvJa7FxPeW4d1niyy+09W4N+e2eItFmDZ9Ivw3d0X4LtpF0Aaq+yvwQ7wYIlPBuKZETZRJrbgBNORkc+/5eOikgsaHV1UoVgsQqrWuKDaoAlV4IRyPhwBLKPy4/R33OUFfFvmsgOVQVhDDIsNDHDdCaMiIwgSOEahW8/A/8PRh21fAYsnAJWng6g7yEZKEAzRcVJ3T00ZP0ScRei5B2tm3vEtv7Q1Bs67TeX7PY2LJLbBn8CRNUan/g7uPzFiOL5C3RaLBT/eI515GgAmLdzonZPmmWt6Kw8BZeoWan0uZzc2yebjAYCft53AS8v+xpp9pzH+nXVoP2MpLmGSvq16aBiirBb0zUjm8y8Fez4cHlGrFOQZ7XEbtOjKWzYyMtWFrBlIZAK7j6kEhssFTv5n0vdy15aeAaqGdI4ATY/ZHcJDN4bJMRRI4IQiQuT75HnAfMqHYMGpZSzehTv81wvoa1muqzvQz8StRkICxygiZfjh15P5GBYhC7DfuoMc6im4kaJtvBARWP08705iER5ewY7eiPYEydqaAJ2HK5cRek8SC456lYq9LSG7rj/06ikF8R/2ap2E8eepTxA7smeK8g6m7r5tktHEMwnfXYs2w824qlxuDncu2qxa/3sTB/omGwz2mq71WOFiEoB/fSDdd8On/DI6FrjzT+DWX6RDrc2G1QqMfkl8L1gsWeRBxodlk97KMzXrag3RyXVulFvNX8UcJ+3M2Mv8P3hDEZIn90jfOwO5kkIQT/YK9e9RIhJGUcl/T225cjmDMXFLYjJC7SmVF/KvUOrlNwR3PKf3qfcwBXwagyAsOF0vF7cf/pOfCoFFaNyD7XUIYqTdBb49+15jPd9fw9cRqksttokY7xHquQ6Df/7ZsX1w8NnROPjsaPz+yMXeEI+/Zo1QT+DF1B1ltWLlw8MAAEXldvyxTxzp9o0nqSAAvHXjud71x0d3x/7/Xo7sHkoCKlgLjkfgxCZI3U8WK9DxYua9RT1uxUyw50LJbC+34ADSe02eLNCoUVRaIu8chSM20OXwFXD+HryhiLIf7pW+d/pJH6xYt5/zzsYsykcfKtbNno/AxUMiWAEsd/srZZwPAyRwjCIUC07laeCV3sArvQK7kurTuyvcGaDuIC04Qg80Oha4KECCNkcIMTgcB9R6ejOxjfn4nuuZWQfZuI2q4hBicITA5Xgg1ZOrpCIYgRM+C46cjGYJODCXFztJCQpzgynVZbGgRWMbbhncHgAwccEGrPXMfP78L3xPsXfrJIzqlYaN/8nGnzMuwW1DO0omaPV73GrUMi4qNoD4nryGIWjksBaaCj8Ch82q+z2T6V2eIDBScsrUtV6t6w66I1Plu+1nP+1TKOeZHSUaqCwQmsvu6AZxvdrXlexbdwTEd7LTrADBxTsZAAkcowglfqN4Px9g5qoF1vzPf9lQbhwfM6KC+VxSPsgbXhAW1SW8ib77lep1Cu6KYG4ce5lYTpivh51Jmn1YVhZJR0P5e/gKFpyYONGi8MVN6uWDqbM+6BmvoHDM91wiTqFww7t8rM3Jcr4H9r9x/QDwE7Cycwwp1x2iBScmgc9i3e0KYNhjQLOOwfwC88Feh4oWHM/+5sxok/xF4rqPi0rHrMBGJfoLR94Xr9uIEdF/fSotk/MYP1qpqrh+5znQA90ti0X0Vzfbrm39MvB36xWDE0p8Z5WsIx4oeaVBkMAxilAaEnZm7j9fUy8HhNZTkseeyCf5O/k38NPD4kSUwZrGBaEkzMrt72b3xssE0QsrOaJ8rL2vA6wxfPK/pLaeY6iSNSJ+zkPJYU+djYFDf6iX+3ISMLu5eMyG9Xb1dUE0b2zDE1f08Nl+Tf/WPpOEBl93ENddTAJvhRu/CBj2aPDfYzZYF9TiG4CC7dL9+1fyS2ESUR9kVi2fh6MJEv0F694W2Pg+8NVk4Jh6HJhYVR2uOyUqTgLr5gNFO4Gd34XW0eg4TPo+VIHjN2aN6XjWlvPii6VwJ59BWXCrGxYb6Oe6k3eWyUV1lhFKQ/Lr43Wrl9+gXra2QvpeLngWjgY2vAN8M9VTVZA9GkGICJlCz7/bt8yFj3i+UykPToDjTWwjdWVc+y7wyD9Aai8+rkOoN9iI/8MeAWmNAnpeI25nJ+C0lwM7vuEbpp89xx7GIGNt6hbP4eQhHfDCv/p432d1bO613tSp7mCG5ceYOL9NKGScL33/1gXA9m/E98K5ik9W/jx737ndviMRIyGnTEj1InDHa+l0YPtXwKq5vvtrSoE9OaLlyx1E2wFIc2gpMa+/uO52hmZlSWwjfc9OoKqEPLDc33HLxULOTOn7j67iMygv8sQg6mbBCSEmyUfgkIvq7CISMobKA9bkF6GQc+GQJ6ZG3qNRc2kVeWJ5ojyxIOeMBDLvkJbJvN1Tp5MPyAsmnkU4PjbWBuDFTlwSvy70zkKx4Bz4jV+2OAe4iLEkfHS1OH0Ae24EM7FRw8T1mvNFFu9y3cAMb9DyZ1PPV/mQP4K0SgojWcycwC8UOgwFrlsoDYr/apKY+FK49ruojGRkY0dCffCGSqQkthNgp3gRWHQd8Nk4YN2bnqqC7GgIlg+1AGA24DjaFtoxC9e01dPmHfrDfy6cUKxD8tihQpkFUHB7CtnB9ep4hRLcLj9mclGdZWg5jxHHicntQukpyS047HxOat/D8tl45XK2RH4pNOgWCzByLtD7et4Mf9c6sQwgWnECHbMwykvuSmOJYSw4wc5FJTzoO13iK54EK04tc8Om9fXUGQG93XrVrXFAb6gWHGHI/9lAz2uAy56XbhMmdRWW8mtPgH1Qnt4bmnsjVIzIgg74vz7Yh2GVgsARAnq3f80vg7XUrvwvv1QbOcVaml0O3we6z3lnUEpN4W+gQihuRnlHMmOQelnAOAtOsG41QDnAOwyQwDGKujYklihff/ZXtwIvdAKKD4R2ER5eJ30fMPGV7IZno/tZhAakuRi8CqsVGPsu8Mh+oFV33rojjA6xy4SW2s0ulPPn2oitgwVHEHotu/lOwCc0EOwNemQd/x8YZgrWKwZHxxEWwcxVdrZYcARY9yfAX5/sdWprovw5VuCc+MucMTihtEv2ABYcAaGjE6wFh42vS+vnuz+KGX3okLcfAH5/Cap458hLErcpjZgTkE8HE0xQvjDCM2B8TwR0vOSCJlBmZ4MggaMXW7/gXR5CwG4oDYkwqSTA38yspcVRw8eG1FbwPRofM6KfeuUXoeqN46nEx6yqwom/+KXNT4CqxSI2UHJLklqDLfR02VwpcrwWnCpZz87PQ4ANMpYHIAp1yBN3VZ40zoKj+TQQCuua1K3yPXLORgsO4DtpqKMaOLZJfB/fFBh0u/je7eIDbdkHfpNUhXs8wP+4dznw8bVA6VH/5QD9rulQjlk+Ga+aK1w41mAtOO2H8sv4ZsqBxmwwuKPat0PnDyFQfPgT4ra/PlMuC4RmhRPEQZInsScbZMz+dsE9FgkdL/mzZdXciMhmTAJHL76Zyrs7vvZMLxBKQyJXv+wF/vfP4rrFqnBT+rtxPA9twV3ENqRKN0YwPUX2WJuk+S8rNDJyC46aKVjwc/ubBVgQTfIemNqNzs47lZjmmzVX+M1KPmWj0tqHY0Z4Pev2WnCCmc25gcFarTYtAD5kUihYLPyIQIHdP/KBtiwlR0J3US0aC/yTC7yrkvlbUlUEjAyUtwdqVhxFgeOnXsE6m/2UaOkFxAcvm5xO3kHyB+sKb8O4jza9r/6ZUO5xoU1N9ExczObCYTteQjoB3QY/1OHZwrLkdt/gaoMhgaMbnovh0Brpe8D/xc2mF4/1mLBZvzRrdYm2hRaAKNQrDE+VDEdke02eWI1gejSlTIKnpu39lxWESvkJ6XY1geMIwrXBiqZTbFpztRFfjPWGNS8LHN8s/W6B6jPGjaIyQyI3vsLg6vb+j2eZBQeQPpDkWb0BqZVn3Vu++3+4t+5BxhVBTCKrWx4ceV0hxG+oTdoo1Bmsi8qbJLQR0OFCcbsQA+hiBI6Pi9sP7H/KuuXlx1q0WxwBF4pIFer3Cpwz4j62XRLmLNM1Dw77PojcYizbvgA2vqfd8dQBEjhGEWpvFwCSPEMR2R6NvPcSUnS+p+5Gnlmn2YaFvYkUewZqdXrqaJIWOCutYG2RJ9WrlwXHI3B+f1G6Xe1GF35zcltxGxsMenQjP8pKPiGno0omFtQPKWQoBufsJYoRffI5qQR0HUXFilSdkvHJv0eO3GUtjxX01iFYcILMWC7c67GNpQHFQnvKjq6S398CSiOwWMuPmmjf/BHwRqY4pYPPMPEgxIIwcWsV0zazzwcli1a4YnDk7aXA0Y3aHU8dIIFjFME2JKwSFm6ezyeI21hzZa2CWTUoC45H4BTtBE7/w6+f3iuWc9Xy9QTlogrh4aWWcCuQBcefwIlRG2Glch6UHrbnjJSW+fBK4NBa2bFU6xiDE0IcVeiVM6s6ChyKwQmNzpfyy0DnpNMl9ct0Heg/j4Qg491Lpe/VHpahPtAFgWPzTPMiJAWd189jXWGEypaPlTt0SnNBCdezNZrvDF46x7eMMIJLyFAdkqVdZsGpLQeeSga+uV0qcIQYIqNc56G6qCIAEjh6IL/InPbgL0JB4FhjgBP5vvtZNa8U+e/vItzsmcepVTdx22c38EshR4dA6RFp+njV4xXS8AcRX6FWZu+vyudEyOjsb5h4rIpoApTrVMpuqjRcd89Pvp8LtSH5+xfgxwd8J6ILeJxmicEJ0r2x9xd+eTZacK5bqLz9kv/wS0uAJrhRy9BGUcmvpUCTNUZC6gN5u1BR5L/OkC04nvYjgRm8seVjX+uMkkvepXDvCtsE65uQRqIl067Kf39IYsHTRjVmJ7jlgK2LxSSswnfIh7cH6pSe/Bv49m6xY+uPUCzL8ikwvJ/R2i0eGiRw9ED+QLOXh27OV7N2sBYcR1VopmCBI8xwbyFuRe4HZ28kfwgxLfIZkJXwJ1T25Urfszdt41T1z/kTVkrnQim7KZujR0A+eZyPBScIPr2eDywN5Ic2ykWl9VxawQo+4dqQz7F0NtDzGuDcm323C5ZHtekaBCuro8p3iLE/17E8wVrAXFdG9f79ILSXglDY8I64j83iLLjAg21LhfQVQrCxMNpTOD552gslS7JSbIkgjATrm9CundzNxAnJBU4ow8SFGEyFZwA7Cg/g27NQJgH+ZCyQ/wnw5mD/5YDgRarf/5oETsNDKW110BYcwSISx2falcOOqFIMjFOp+/gWcb2V7zxEPsd8PF/9GFkEH3PB1sBl1UQb4CsoDjHxCC27+qnTj2hSCrL0zmzNCCOLBZiWB/zrA3FoqRyfURYBblzW537mkP+yuib6M8hF5Ve0ex5g7Ydo+/1m4bLnfLcJD0VbY+D6j3z3e9Mf1Pje4/IUBixy6wf7UFciFNHrqAFWv8BPmxCIkOI3PL+HDfoXXB5sbKBgAQzGguN2ixmSvfUyZaOipRNERsUqP6iVRE+xYP3w1Mdahv7xdNR8BE6Q7bTLKYohf+2lgNMus+AEKF/q6ZD6u4a8dQXpZmQ79HKLZKApLHSGBI4eyDNnygVOML7MmHgg+/9898stOMHG4JQxI5eyn5TuO7nH1x2m5gcP5rvU8JewT95T+vAKcd1fz18tWRoAvNxd+v7MQeCnh/h1IYBPoEVnfrJQtayhzprQervLnxLXA1m3dJ1NXKdRMvL61M4Hx4lWBHbqgrMJJSsj2ztXEtXCveKoBg6vl+5TyxMDAFs+kb7//Ebp+5pSYNePdZtAdvlTwMqn+WkTTv7tv2woQ4yFhy2bjE/4jWx751YakaRSr71M3CcInHYXiPvlkxhHxymLPSWBIzzQhbZasLYBwIZ3fY9bqR61c81OtKw0ytPnWEK04LDIJ/EE+DZyX64nBjPIZwsrlh7aJ/sOP4kbDYAEjh5s/lj6vqo4BAuO58aOjgfiPK4T1pIjGTKolLtB7Yb3iK6OF/uKgjey/CepUmPbl+I6m5xQjUYt1fex0zeUK1he1Oh4UfBlX+0rrgsBfHJYPzoA9BkvHlOwItVeDqx9XXzPZkxVwqipGsJhwWGH/ArX89nIvVuk723Mw0tJ+Anuj9P7pA89QDlWRGDNK77bhGSjAPDTI/yghTXzPHWFEIOz/k1xfcc3vvvZFBahuF2FByR7LwuWbPYhLJQLJtGfcCzR8eK5HPeJclnh+wQRwrZlSnlcvG2p53jZ6/rvHH5mchZnbfDzOrEjZq1RfJC5P3wsOCHc40un+25bMAr45Fp+JFuw7ZIg+CxRQKPmwD2bgz8GnSGBowfyZF3OapmrwE8DtXcZv4xLEs197AUscVFVBq+yBd+8kG34dsZ0GEoGTxZW4Ez8NnD5Cx9W38dacPb+Kq5HxfqWZbE1Aa6eH/i75Vk1ldx/ANDrX0DHYXxczn1bRSG09nXgzAGxnL+GRN4zYk3hSph2mDh7TauUYQWOvxishk6zjtL3bIJJqxW4ch5wCZMVVxA9Sm7WUOJbADGglPMEqgLAKs8on7oKYPms3+veBJ5tK+by8cmhEoTVmk0E+YZn8ldJkjtP7EswFhxB4LBWkIRm/Bx5Srid4oOajYtaoTBC6qCn7WSneWGtcJsWyI6lREEoqRy3MGReGGXXZaRyOQG5ZTnQPc6ejx1LZMdZKuYo++15hf9QpV2Sj0xt3sn/MRgICRw9OHNQ+l4+igpQT2MtNF61FeLcTcJnC7bJXFTVwft25RP8pfVRLmcNYG1gYR/cTTsELh/bCLj+Y+V9tVX83FpPJQHf3xP8MQBA/xuBHler719yJzBbNlJKbqkRsFqBid8BM48ATdsBjVupVOqnIZH/1/IRWT7ldXRRIQgRUueqg3B/Ca6GQEkgz3YG3Axc+JD4Xm0iTiB0ASxcXy6FnC7BujDVZuQWyJnhWT7K1xmK29WbRoBxYQsPerazIIxeCiYflZB/RS4Q5fczm0la+E7WJb5vubT8H/8TO3bs+WStV8KEvQLVJQouKrXj9gQRCx3R/hOk+8e8BTzFfNeZQ8GPKgOkeZfksJanf1YEH4MjPJMiMA0ECRytUUoz7qpV6KWr9MKEm6znNaIF58wBPleEMKRbQMlFte1r/8fF+ovlRMUC3a9Q3+9TJ9M7D2aYOAD0uAq45HF+/bwp4vZjeXx+iroy7DF+Ke8pA8pDGOUxOGqoudWCyfsioORLP7IBWP8OFPMNmXGqBrVjFoJe5ZOano0MfZBfDp+lXqZVT345yM8oRjULjkshXgQQH4CslbSZp5cdrAVn1/cK36eShr+iqG5BxvIHpNsts+B47iv2gW4vlbrgBJTcLwA/txdL9RmxUyd0AtVi5k7ukcbWsdZlpUBygZoSX4EjT24oIAzWECbutDUR8+xccD/QT/YM+OWx0AY/yNsm9n+xMMKuWcfg4jtLDosjbtn/Kmsav8x+yv/x6EwQY3uJkFBKM+6s8b043E7l2IyKQn4Z21hqIVn8b1/rSm2l741Tepi/ORrLHsxCsFcjPwLH1iS43uE/K/jRGcIQcSBwnAnL0IeAntfy2Y83eoLyivcH/3klWnUDHtjBm5efYRqxg38ol5dPhKiGatyQvwZbFjBdvJ9vrFm3xPseE3RyhsJ3mCQGB0H0/k/u5pfBpBFo6FzyBNBvgrIIF5iygm9Dktrw97t8eDGg3jk6sEpcv3sDMN8TMC8IHzYYVHDRhjoSjqXypHIsW/4iBRdwEAJH3kmqLpbF4AgCR/b7P/kXcJdKFmg5bAZzgB/CX7iDd80Ue1zQ8vvk9D+8mHsjU7q9c7a43m4w/98q5Q6rKvZtp399HOg5RnzvrOXvlWN5/Ht2oMrge4Buo5Wvmyap4jND6djlyNumwh1Aai9+nU0xkH6u77NASZR9dLVy2z3iaWDgrf6vdQMgC46WuN3SeZa6Xs4vnQoWHLVemDBiItqm8BlPYyfMMuuoVq5HaZZwQXixD9M7ZA//qtOBLRtFu4GPr5H2ZELFYuH9tGyPzd9oqGBJauPbSK5XmN8nFNRcVH6zUSsMwRRiH+Sc3G3iGJwgxJPwgBKmHTmbEa57f1OaxMTxotdiUc+Ro9Z2CElALVFSF5fQbrAWHCGoP9h5jNh7Vejpqw1BP/1P8LGB7HHJLTgVRVKrgEspBgdA0Q7fOoW29/y7pNuT2wJj3+etIaNfBrpdIXb6hOle5PW9NsBX3ABA3/HS9+dN9i0DAEsfVOiIHpG+/+5u4G0mjqeS6dwqXTcpvfll18uDt+C4Xb5uyreYkWU1jMDZ/pVvO6Qk3tQ6psFc6wagq8ApLi7GhAkTkJiYiOTkZEyePBkVFSqmOU/5e+65B127dkV8fDzatm2Le++9F6Wl0ge2xWLxeS1erPIAMZLNH/IPfwBoN0RMIOdSiMFR64U18WSvjG8KdBmhXKabx42klOhPDeGGYRvN1N68K4wlkOLe9YPvNiEgLlSsUWJPL6m1/5w2dUXpeK+cF/znW3YD+t/ku91e7mdUgcIQ+5VMcCPbK42O831grXk19EBSNcKdB0d4QKkNvyfUUbO2Ln9SebvQwz5nlFSYC5Zf1oJz4Dd+KYnf8COsWfEhuGbY7Odsu9IuK/j4jZoyUYDJZ5t/M0vMvg7wv8/tCq7NEx7WrQf47uv9L+DS/+MFidXqf3Snv2OXP7zVOodlRwNP5LntC+n7QB2C1ufyy1Cy5AfKfSNPb6J0nsOcmThUdBU4EyZMwI4dO7Bs2TL8+OOP+O233zB1qrpv+fjx4zh+/DhefPFFbN++HQsXLkROTg4mT/ZVxh988AFOnDjhfY0ZM0bHXxIkrPWmUQvRDRKKBccblNmOT0Z1+2++ZdjZwBVvHIWLUM1FxfpdgcAjXcqO+m679h3fbcEybCa/dLulQ8Xrw+iX1PdN+IoP6AwWiwW4+nXf7UpxVQKCBaf1QHEba1VjrUox8cr1CK6d+mLYKCqVcyEMqdXCQne2EapbTz41QVtPtlrhwcbmzxEe6sH2/oVrusNFQBePa4Z1WbDxP4pz5KlcH0U7xfX4ZN614Y/yguDEv5LFWg2lMudc5v8zHYcFVw/giWcJcqZygStf9b9fsFQ7q6XWoFBn/GbZKhNZp/b6lik/wVttvpkKFO6U7hPixyII3QTOrl27kJOTg/feew+ZmZkYMmQIXnvtNSxevBjHjysEhQHo1asXvv76a1x55ZXo1KkTLrnkEjzzzDP44Ycf4HRKL5Dk5GSkpqZ6X3FxETDPTRYzY23LrmLEupIFR8mNBIgZfYWgzLS+viKnkdBb4pSTfsmD/9xuMchYfhOyc9X0GR9Y4CgFi7KZPENFaMQPMe6ylF5AjzH8OntOg0X4rBJqo6cCofQ5+fxdAt7/kEnmZi8VHwJsb5hzKzdKWsWsGGXBCZie4CzOgVNX1NoINbwCx3PdeTtYdt/6aj15ZkLt/UfHiRYcYfi52y2N37CX+dYlfJ8cu0ckpfTiLbqD71EWOUKgvr08sMCpKRWnoKmrwPEXNAwAfW/w3WaN4i33AueM4petB6gHgAPKcYKtArRTQszj77LOnD+Bw+ZQYxFGyLGxPI1aijFJcj4dB2z9HPhglHR7XdpqndFN4KxduxbJyckYOFDsxWZnZ8NqtWL9+vV+PimltLQUiYmJiI6WNvh33303WrRogUGDBmHBggXg/NycdrsdZWVlkpcuxCUB0zYB4z8FhjwgPsicdjGFt8CSO3w/z47AYn3ojWRxIKw5uGCbbz1yP2tNiWhulPv1WYEz+kVfM7EctYd6XVG66aasBK55G7hlKXDxf0KvUynzcdfRfO6f5IzQ6wOUe2Bfq/jchQbLGg0Mul3cvvlDfsleq85aZVOw3LIWLC4HsO0roMTTq9NzLqpggowFVwFZcEKn17WhlRdco0KKf+FeFpLmsSLEUaUwnDvIkU7bPSM1hcR/teXSz9rLfK81tViNXZ6hyawAHnS7dP6uxDZiXiB7mX9ryL5cPh+PQDACR/7suOZt/x296z4E+oxT3nf9R/xQ7pt/EEMMHNVAoUI7DfBTXywcHfgY5eSrTG7p7z9kszezE3lu/8q3o+yoUZ5o1O0CTnmyWMsFeLAjaQ1EN4FTUFCAVq2kD+bo6Gg0a9YMBQXBZao9deoU5syZ4+PWmj17Nr744gssW7YMY8eOxV133YXXXntNpRZg7ty5SEpK8r4yMur4kAuGFl34iPeYeLGnozTEUp6dFAC+ZQLiWFdSYpo0u2ZsI9GSslbJfSKz4BR6guai43wD+VgxZGsS+CLNl2UDTWqrXC5Y5Km8//0F3/OMiePnLgpldJaAkvWj/QXquX+CIf1c3237VyqXFfJ5NEmXupqEnBpsw+CTqMtDXcVI/iJeeH19m1ARW2nd6lQjmN6/4MagYeKhM+QBIPPO4Mt7g3U915/QhgjuGjaIFBx/7QWbQ0WwAim1D/IHXdVpX9GuJBiqisUYm5O7xO3RsdK4t0YtRAFUU+Y7DYJA6VE+Cy9LMNZl+ciqhBb+54Hqerl68Gyj5vxQ7g4XinXUlCp3RKvP8FNf1AW1vET+2o0i5hyzqQpO7wN+fEBWf414PbFxTEqj+gQiMJFnyAJnxowZikG+7Gv37vrHD5SVlWH06NHo0aMHnnrqKcm+J554AhdccAH69++PRx99FI888gheeOEF1bpmzpyJ0tJS7+vIkSOqZTVFEBPyxH9q7P1Ffd91C5l64/0/MOS9pV88cS5KQWZyi04oKrzX2OAyGPtD3qieEyBzZzAoWT/qa0G47DnefD51deCyQs8nrY905nbh3LJWs0NrlBslfw2JP4TspIKA1nWqhiDEk7+ZkQn/2JoAlz2rvE+ehRYQz7VwnTVJ45dCbKBciNTKBykEkfpA3kHa9aNMOAn1yq5pxSkkmAl25ZZctoPXqKU4HcKisequFqUpXoKZwb7/jUDv68X3FotyOxiTAIx5M/gUE8J8YkLmYznPtVfefsUrgese+576PrXJfYXJbpPbSjuBv7/Eu5xY3A7frMoAsPF99e+NwADkkB39Dz74IG655Ra/ZTp27IjU1FQUFUlntnU6nSguLkZqaqrKJ3nKy8sxatQoNGnSBEuWLEFMjP9efGZmJubMmQO73Q6bzTebos1mU9yuO4GmGZDTZQQ/TYHQMLG0YiaOjG3EZ7pUG5Amn9FVqfcgMGou39AM9mQPjg4ylun23+tnERE4b7KYDl1IhFZflCw4/jIdB0NCMz42QC0DNYs3M6tsqL8gvPI+ELft/VVM6hZlE8VRqEGJAvKJGQ0LMlYTOB73SCDXJxEa39/nOwJSnk9GSGq3aQFQsB04ukFa3lEZ/DxGStmGAWDVs77xKmpWSTmsS16eCZ0dBRabIB3AocTG9/jh2HXB1hi4ap44kslZ42uheSrEeCig7td8Wt/AZdoPUd+37UtpVmwBwVrfY4x4XwpExfqGNvztmTKHFXtKHgMBrQaJaEjIAqdly5Zo2TKwXzMrKwslJSXIy8vDgAG8iWvFihVwu93IzFTIKeChrKwMI0eOhM1mw/fffx9U8HB+fj6aNm0aHhHjj1BTVwuCSOlBn9Cc90Xby/mofLkFp/tVfOK9E/lA8T/K9Svl1WjeCbhzjfievZibdwFO7+XNrQdkgc6tegT8OUER37RujYc/WIFzxxoxkZUmdQdh9BQeBlE24Pw7+GHfAC9a7AqqVMgvkdKTb/RLD/sPSvRH41ZACdODC/cwcWH0jb+Z5An/dBruG8On1LZ4xaTnXLNJ+OTiBlDIo+Xn+hCmPoi2AQMnA5s8PXlnjRjQK2Av52NL5MiTXfqboy22EdCiK193+6F8OonC7erl6ypuBFjhppTUsC7U1WocTMfY5s/lq5a+ghGp8hQkgri5/EXgJ484KvcMBvI3ez0LOx9XhKBbDE737t0xatQoTJkyBRs2bMCaNWswbdo0jB8/Hunp/I137NgxdOvWDRs28DdfWVkZRowYgcrKSrz//vsoKytDQUEBCgoK4HLxN+IPP/yA9957D9u3b8e+ffvw5ptv4r///S/uuSfE+YuMIBQLjtstzlmklP/CGgXcvQ64L58318r9nd2u4MUNwE+EV1UMfH4jkPehWCbQzLSAVATd8Tvw0D4+I6WcqAjOTMs2osEEGWqNYHqPtgFts8Ttrlpgg8KQesHdYC8Xz2tdLTjyxi2cif44jnFtkAWnzkz4EnhINmRXKTbNIbPgtBvsv175ZL3+Lg/B2sJxwOBp4nZrtBjjJ3Dwd16ky5EHrbIWnO5X+pafuooftHHebcpTzKglQqwLrMVGsJYnt+OXGeodcr+wgbz+aNWDz8KudCz+uG2F8na1/5ENFE9qA2QqDHRJaOZrAU/tHfhYHjngZ96+8KHrU2rRokWYNm0ahg8fDqvVirFjx2LePDHJmsPhwJ49e1BVxfc8Nm/e7B1h1blzZ0ldBw4cQPv27RETE4P58+fjgQceAMdx6Ny5M15++WVMmTIFEUcoFpz1b4oPDLX5otgewaE10n1y8+LzHpMvm+hO7itXIj4ZmPg934DGxPMvf8OuI5XJy3jB0CTIRkZLvNMTREkDvmsrlHNLCJzeKyY+rGsMjrxxC+dUDWxPmCw4dccaxT88YhJ8XQss8iDjuCQ+ONeuct/L57Lzl0BP+C/bDxVHNAH8dSrsa5wiHWosR+62EgYYxDZWHi0Zm8AP2gCUO33Z/wd8P413/Qab8NQfU1fzrjBB4NywGNi3jLeO14VgH/iTl0mDmoONg2Trv2Ex8Nl49bKA1HUOAJfO5p8PbCyULcm3cxXMQI/6pArREV0FTrNmzfDpp2rD2YD27dtLhncPGzbM73BvABg1ahRGjRrlt0zEoDRza9Y0ZT/mimfEdX/zRdWHYB+aHS+Svg9zuu06Ea7MuWy+j8TW0iRc+1cFHvYuZJ0t3u/fzx4MLkd4LThsRmey4NSfCV+KQ4qVYlyEXFLxyeK265ns6gItu/OjlhzV0odZ0U7gxFbl2DpBjDRtJ5089vQ+/gUAaf38D5SQH/NvHjdWn3GBBbBSmyg8VC0W6aXd4SL+3usa4nMivR+AfuL7lB78q64ouaimrADeZSzpia1Fd9Olc/gA6mDnb2L/52AmDxaCzIX/L9rGi8RvbhPLCMHcLIFSVkxQmeA5AqC5qPREPov04Ht9Y2de6g48lSQN0PI347fAmDel74MRIYGyhAaLfH6Xsxn5cM1188X1Ri2ALsyoMFsi8LfnAaA2DYcwgWmdc9YwLb2jSvpecwNOgAoFl4nFWrfh/oSU5l3EdZ/M6G7RtZDIpPnvdAnQ99/SssKDUe6iAoAf7vP9XpdTHLmU0EJ9ZFKgmBO3Czi0lm/vvmYeqn995v9zwvfKEdz0covDhQ8D17xZ/4EF9cViEfPlWKzAkOm+6SZSmPjAC+4FslWm4VAitjGfUTm9v1QUqYUPCMPr2Rxr3a+QllFKyFl2VHpNsVw0Q8xsHYGQwNET1j0yfTcwYo60oa8qFgO5WNgLUI1+/wZmscMlAwicO//kg1i14OLHtKmnISDPcbSCEZExjXgz+8OeoG97GTNsWiVnRFdPinh2KHkosI29o1pnC06AETiC6Ztzm9MKGGmwoyMrCqUB6zUl4n/fTDYi6RpZZ0hwh5zeF1y2ZG/eGYtoNRmlkOnXb+Ar+OtAyH677Utx+3m3KZdnUbLgxKoIKnlem3By7Tt8Oz2rmBcvFgtw2QvS/XXFYgFu+pZPjMoGSat1joQ58NicajHxQL8bxfeJCiN4o+OBQSohIHp5GzSCBI6eNGFGMQgBcWzgccFW5c8FG8DLBtM2bade7sJHtBM3ACVtY/EnRAQRwwpWYaZiea8a4OflEkzEdRU47IgHe0V4R1F9MVHb7zvbkad/mNsaeDqVd4sKyfzikpRj/wTLQXI7MSeRkJ5BgtI8dp7RTvFNReuN0oSySte0pGqV6y8YS4tSQLGaoAp19KreWK1SgS9kAgZ8rfyhYrHwL/aZkTtbuazXCieLl2E/G5cEpMpclNGxwPEtynVGYGAxCwkcPWmSAlz7Lp++W0gOxQqcjzQwod78Az+0r/0QftSBEhc9Uv/vmbycn0xt/GfUG2dhLXLlhUBPTybV6DjxQaJk0lfy7dsSRTN/XQSOs1YaULppgb5TNbgDxOCwwYuEBqjEOc3PBCo8Ocfk07oI3LCYj/+a/Kv/LL1KGXK989gxvXWlOJE25/mP13A7pZ0+AX+B0wJKLk613xGMBTycNGcG0BjVlrrdvJUP8D0/af34pSC2rnkbGMrm0bFIYwlZ1K63CCGCx/o2EPpcL33vT7H3/Tdw7k3q+5XocCH/AnhfrEB0HDDyGT74TIv4h4zzgLv+rH89DYH4ZqLZXug5H8uTBg+yqdCVSFLwaVus0pT0oSJ/UBxZL14bAHR1UfmrW82VQISG2iSTpYcDz57dJEXs6PgVOAqWGSHAmI2D6XYlkP0U7w4q2A70m8BbKvyNZnpnGC+S5G55+dQyakz4ik/o93cO/14pKel5UyLPgiNn4K18B6bzcOO+014qdnDiZRacfv/ms0B38OSxEYKrf3+Rfx8dp5x8FghPGo4QIIFjNL3GAt8q5B8AfH3ldeHq+Xzq7axpfJZgQnvaXyAOv9/2FbD8Kd8y/gIuhfm7rpwH/HCvuN0axVhw6iBw5FNx9Lle56kagqy7to7uNkJKkxTeSiIk3RNIbgd8NYlfbxzEA8ffnEFK+ZeE9PzsvHFWKz9XFsC3acFQflw55rDDRb7blOhyKX9/CAJH/luzpvGdukgnOha46GFjv/Mn5vvkU01E24CLZ/p+5qIZwPHN/ICIlJ7A7h99ywQ74itMkIvKaNTmMblJYW6ZutD/RuDeLSRu9IQ146tlV5XHKbHJEkd7ekYDbuazOLc5j3/f7QrRglMXgeNQ6H3rGWTsLwtu8QFtv4vgue5D321s5upgMmD7y7OiZCUS5lJiY0fUOE8WjDo8wKigp0pDSxoqJJ0TRvWMfkncF0wi07MVNqg7WC6eyacmiIrmYzxbMXGc1mg+03Qwmd3DCFlwIoFHDkRsoiRCgS6X+s/3AfjGu4x+WWzs2fwVAHDrL7x7ydakfkHGcguOoxrSYeIGjqL6iEmOJnGTEfUiUHxJQhDxJ/6mIlBzgwFijiZ/XP4CsPFd8f3Q6UDu/ymXVXN7+CO2EfDYcTGWkbUgCJmHCf5+1Dq+p+c1/CCJtoN54RPsvIVhJLLlV0NlwC3i+pDpJG7MxsBbZUF4CsjjHCwWXtjIxQ0gdU0JyzrF4MgsOD6THhpowRHy+QDANfUYCktIiU3wP+dPMDER/kZUyl1UrHCdojI1AIvFAty1jrdGTsrxXzaYSSWViG0kxhV2GAb86wPg318CLTr7+9TZxc5vta8z624+AHnsu/wItkierscDCZxwwDZQHfw0VkRkYo1SzwshcM5I//vVsGlpwanSOQYnSOuQUm4Nou74i1k59+bAn/eXSFQucMoLxHVh2oRAtOoOjF8EtMtSL9PvRuCKV4Krzx9WK9DrWuAclcSZZxPXMpazbV8pl7lhcd3rj00A+o5XHiARoUS+BGuItB8CJGXwvRB5ZkvCHATKNq2W7TUQ9QkylltwHDXBzxZdF9RGUbEPxXNMMq2KmThvMp/9t8NQIG+hdF8wbolYmXXx/u3AK56MusJQ4qc8oz1bdhfLBTtHkj+S2gK3LQ/PHHENnXYXiOtCAsd1b/KjKYU5ySI8KFhrSOCEgyapwAMqwamEOdDLPCtYcBxVfMBoKN/DJvkT6tDTguNWicHZvVRcl08pQtSfhGbAvZv5dbnACWYG66YdAFj4a+3hfb4DH9jZwU/u8nymfR0PliG1N3DHH/Wvh1CGFaBCUr+cGdIykT6EXmPIRUUQdUWY4C61NzAtr+6zDrOw2VlrK9TLKSH0vgUcVTKXg0GZjIXEcJ2GU3yZUaSfCzz4d3BWlsQ0fqTltA2iuOl6ubhfKf6ruqTuxybEo3W8uO51EIFh//uSI+LwfhalCaAbMGTBIYi6Mv5TYN8yYMCtQKPmfLDjpgX1i6uKtvGjVdwOXuAoBSWrIXdR1coEjlGjqISkc63J/WoYaX1Cc/vI56tiM6z/rJCjRS6eQ+HOP/lJZgcEER9E1B12VJO9FFg6XaEMCRyCIIIhvR//EoiKBjKn1r/e2Eb8A+XIhtAC+gSBIwgkR6W6G0kL1OJ7AmXVJbRj3CfA5o+Aix+vXz07vxPXC7bVry45zToA56skNyW0I5j4KxMM7dYSclERRKQhuKa8szgHiSBwhDmD5BYcPV1Ugngq3CEOUVWaIJHQlu5X8jlJgslg7BeNrw0iPARKX3GWWXBI4BBEpNH3Bn75z8rQPifMIyQIC0eVvhYcpRicNweLm8iCQxDGMvwJ//vrOrrTpJDAIYhIRRgJESwOmcCprQx+Qsy6IBFPCvvJgmMeUnqF+wgIrTj/rnAfQcRAAocgIo3O2fzyzCH/5eQIw8Qbt+KX8lFUegYZgwMqT0v30wgq83BbLjD4Ht/twjxp/mYgJyKLi/8DXP+ROF/XWQwJHIKINITh52VHgX+CSI8vIMTuNPIIHKNjcITgYoHEdG2/j9CPmDhgxNO+28ctAjLvAKaE6C4lwoetMdDjaiClh3R71rTwHE8YIYFDEJFGKuMu+E6hV62GkgXHpaMFR88syUR4GHyv9H2TFOCy54BW3cJzPETdGf0ykNyWX08/F7ikniPtTAgNEyeISINN2FV2NPjP2T0WHEHggJMlC9RQhHCctD5O9l1aJD0kjGfEHH4yziW3AwMmhftoiPqQnAHcr/GQf5NBAocgIpGuo4E9SwOXYxHmr2LT9bOTdkpGPdUTifUGQGURUMUMa7/+I+2+izCWvuOBdoMphoMwPeSiIohIZPgscT1Y15IgZuKTgWiPFUiYdC+UeoJBLpb+WQGUeIKiO14cXNIxInJJbsvP1E0QJoauYIKIRNgMxvJJNNUQLDi2RDHZHzuzt6YuKpfvtp88ScZCmV6CIAhCJ0jgEEQkEttIXN/+deDyHCdacGxN+BnrAWkMj5YWHLmLikWYEZ0gCCKMkMAhiEiEdfH8cK96OQFHteg2im0kCpySw0whHV1ULJ2Ha/c9BEEQdYQEDkE0BNiZxGMSAIvCra1pDI4fC46Qx4cgCCKMkMAhiIaAo4pfRtn4+Wa6Xq5QSEsXlR8LTnp/7b6HIAiijpDAIYhIpddYfhlMThnBgiPk0BFcVHK0suKoWXBsSWfdhH4EQUQmJHAIIlJpmxV8WcGCI8wZ1HqAcjnNBI7HgiN3hSU01aZ+giCIeqKrwCkuLsaECROQmJiI5ORkTJ48GRUVFX4/M2zYMFgsFsnrjjvukJQ5fPgwRo8ejYSEBLRq1QoPP/wwnE6nSo0EYVIEa4yzJnBZuQXH1gSIS1YoqJHAEUZRWWTWmniaYJMgiMhA10zGEyZMwIkTJ7Bs2TI4HA5MmjQJU6dOxaeffur3c1OmTMHs2bO97xMSxJlsXS4XRo8ejdTUVPz55584ceIEJk6ciJiYGPz3v//V7bcQhOFEx/FLNoBYDbkFBwDimwI1JdJyWruorFGA2yFup1mnCYKIEHSz4OzatQs5OTl47733kJmZiSFDhuC1117D4sWLcfz4cb+fTUhIQGpqqveVmCjm1fj111+xc+dOfPLJJ+jXrx8uu+wyzJkzB/Pnz0dtba1eP4cgjEcQOE574LJyCw6gknBPZxfVqT3a1E8QBFFPdBM4a9euRXJyMgYOHOjdlp2dDavVivXr1/v97KJFi9CiRQv06tULM2fORFVVlaTe3r17IyVFnG9n5MiRKCsrw44dOxTrs9vtKCsrk7wIIuKJEQROMBYcBYFzfItvOa0sOGouqoxMbeonCIKoJ7q5qAoKCtCqVSvJtujoaDRr1gwFBQUqnwL+/e9/o127dkhPT8fWrVvx6KOPYs+ePfjmm2+89bLiBoD3vVq9c+fOxf/93//V5+cQhPF4XVTBxOAouKgU0diCY7UCt/4CLBjJv7/kcW3qJwiCqCchC5wZM2bgueee81tm165ddT6gqVOnetd79+6NtLQ0DB8+HP/88w86depUpzpnzpyJ6dOne9+XlZUhIyOjzsdIEIYQXY8gYzX0sOC0PR94qtR/eYIgCIMJWeA8+OCDuOWWW/yW6dixI1JTU1FUVCTZ7nQ6UVxcjNRUlRwdCmRm8ibvffv2oVOnTkhNTcWGDRskZQoLCwFAtV6bzQabzRb0dxJEROB1UUWwBUcpYzJBEEQEELLAadmyJVq2bBmwXFZWFkpKSpCXl4cBA/icHCtWrIDb7faKlmDIz88HAKSlpXnrfeaZZ1BUVOR1gS1btgyJiYno0aNHiL+GICKYkFxUBltw2FFUBEEQEYhu3a/u3btj1KhRmDJlCjZs2IA1a9Zg2rRpGD9+PNLT0wEAx44dQ7du3bwWmX/++Qdz5sxBXl4eDh48iO+//x4TJ07EhRdeiD59+gAARowYgR49euCmm27CX3/9hV9++QWPP/447r77brLSEA2L6FCCjAULTiCB42eKhVBQCzImCIKIEHS1Ly9atAjdunXD8OHDcfnll2PIkCF45513vPsdDgf27NnjHSUVGxuL5cuXY8SIEejWrRsefPBBjB07Fj/88IP3M1FRUfjxxx8RFRWFrKws3HjjjZg4caIkbw5BNAgEseJ2Aq4AiSy9FhzGRXXN2woFNbLg/J3DL8v9p3wgCIIIF7om+mvWrJnfpH7t27cHx5jMMzIysHr16oD1tmvXDj/99JMmx0gQEYtgwQH4OJyoxupllVxUvf4FLLldWk4rF9WqudrUQxAEoRMUIUgQkQorcKpO+S+rFGQcpdR/0XBGcYIgiAiGBA5BRCpW5vbMecx/WcGCExtgFFV9LDguR2BXGUEQRIRAAocgzMCepf73BxtkXFcLDscBb18EvNoXcNKUKARBRD66xuAQBGEQwebBqasFx14OFHmmQtn+dd3qIAiCMBCy4BBEQ0AtD875dwEtzql//WyywQr1qVYIgiAiBRI4BGEG4pv6369mwRk1F7ibyfxd1zw4DnHCWxz6U1zvfGnd6iMIgtAZEjgEEcn8awG/bNrefzl/mYwtFnG9ri4qB5NscO+v4rqbgo4JgohMSOAQRCST2JpfVpf4L6eU6E+RugqcKuXtzes2AS5BEITekMAhiEhGcE1Vn/FfLthRVKf21u04HCrTRVzyRN3qIwiC0BkSOAQRyQgCp6YEqClTLmOvEF1FgQTOwsvrdhxKAiehORCfXLf6CIIgdIYEDkFEMnHJ4vpvLyiX+eszcT2gi6qOKLmoAgU+EwRBhBESOAQRyUTHiut2FQtOTYm4HhWrXKa+KFlwSOAQBBHBkMAhCLOQ0kt5e+MUcZ0dMaUlShYc1rpEEAQRYZDAIYhIp894fqkW6Guv4Je9xup3DErf3fEi/b6PIAiinpDAIYhIRwgcVhM4tZX8Mraxeh0tu/HL1gPqdgxKFpxzb65bXQRBEAZAAocgIh0hcNhRqby/tpxf2pqo1zH0QU+ZxLodg5K48ieoCIIgwgwJHIKIdGIFgRPIgtNIvQ5rFL+sa+Zhpe+2UvNBEETkQi0UQUQ6XheVSjbhoARONL90u+p2DGrfTRAEEaGQwCGISCcmgAXH7nFR+XMZeQVOHS04+Z8FLkMQBBFBkMAhiEhHEDjbv1beH0yQcX0FTlLrun2OIAgiTJDAIYhIR7DQyNcFDv3JL/WMwXHW8su+/+aX506sWz0EQRAGER3uAyAIIgCJaeK60y4dLeV2Ay47v24LxoJTxxgcZw2/PP9OoP8EIL1/3eohCIIwCBI4BBHpdL9KXBeEhgA7dLz1QPU66uuicnksOLGNgLQ+dauDIAjCQMhFRRCRTlQMYEvi15126T4h/sZiDXIUlaNuxyAIq2hb3T5PEARhMCRwCMIM2Ev5ZdFO2XbPNA2xjf3PQ1UfC46jRrTgRJHAIQjCHJDAIQgz8dWt0ve1jMDxR31icAq3i+vxyaF/niAIIgyQwCEIMyFYUgS8AsePewoQBU7ZsdC/U/iO5La8u4wgCMIEkMAhCDMTTBZjQBQ4AFBVXLfvaJwS2ucIgiDCCAkcgjAz9iAm2gT4IGSB4gOhfcfRjfwykIgiCIKIIEjgEIQZaDOIXzZOlW4PZpoGOfKh5oE4uolfuuo4AosgCCIMkMAhCDMwbAa/bNRSur3GM7oqlODfUAWOIGw6XBja5wiCIMKIrgKnuLgYEyZMQGJiIpKTkzF58mRUVFSolj948CAsFovi68svv/SWU9q/ePFiPX8KQYSX6Dh+KRcngsCJSwpQASeuynPpBKLqFL9sd0FonyMIgggjumYynjBhAk6cOIFly5bB4XBg0qRJmDp1Kj799FPF8hkZGThx4oRk2zvvvIMXXngBl112mWT7Bx98gFGjRnnfJycna378BBExCAn25OIkWIHDjn5yqsxKrkblSX4ptx4RBEFEMLoJnF27diEnJwcbN27EwIF8CvnXXnsNl19+OV588UWkp6f7fCYqKgqpqdIYgyVLluD6669H48bSGIPk5GSfsgTRYBEEjquOAqdZR3HdEYLAcTnE7yCBQxCEidDNRbV27VokJyd7xQ0AZGdnw2q1Yv369UHVkZeXh/z8fEyePNln3913340WLVpg0KBBWLBgATiOU6iBx263o6ysTPIiCFMhZBCuKARmtwCWPsS/t3uu5YAuKgA9r+GXtZXqZTZ/DLw2ADi1j39fdZpfWqxAfNPQj5sgCCJM6CZwCgoK0KpVK8m26OhoNGvWDAUFBUHV8f7776N79+4YPHiwZPvs2bPxxRdfYNmyZRg7dizuuusuvPbaa6r1zJ07F0lJSd5XRkZG6D+IIMJJTLy47nYAG9/lZxIPOgYHQFwyvxREixLfTwNO7wM+vJJ/X+mJv4lvBlhpTAJBEOYh5BZrxowZqoHAwmv37t31PrDq6mp8+umnitabJ554AhdccAH69++PRx99FI888gheeOEF1bpmzpyJ0tJS7+vIkSP1Pj6CMBSlHDQb3wNObOXXgxE4iR638OG1gcsKgcXF+/mlLYRh6ARBEBFAyDE4Dz74IG655Ra/ZTp27IjU1FQUFRVJtjudThQXFwcVO/PVV1+hqqoKEydODFg2MzMTc+bMgd1uh83mOxmgzWZT3E4QpkEpz82f88SAYVti4DoateCXh/5U3q8Um/PtnfzyzMHA9RMEQUQQIQucli1bomXLwMGGWVlZKCkpQV5eHgYMGAAAWLFiBdxuNzIzMwN+/v3338dVV10V1Hfl5+ejadOmJGKIhkt0rO+2UsYS2bxz4DoyzueXliiA43xnHxdGSwFAkzR+Waue1oEgCCKS0W0UVffu3TFq1ChMmTIFb731FhwOB6ZNm4bx48d7R1AdO3YMw4cPx0cffYRBgwZ5P7tv3z789ttv+Omnn3zq/eGHH1BYWIjzzz8fcXFxWLZsGf773//ioYce0uunEERkkJQhFTUswUyjkNyWX7rsfGxNY1nnQXBHAUDJIV4ECfS+LrRjJQiCCDO65sFZtGgRpk2bhuHDh8NqtWLs2LGYN2+ed7/D4cCePXtQVVUl+dyCBQvQpk0bjBgxwqfOmJgYzJ8/Hw888AA4jkPnzp3x8ssvY8qUKXr+FIIIPw9s55cHfhODgAXk1hglWBF0dCPQ7XLp/gqpSxnPtRPXh1IHgiAIc2Hh/I2vbqCUlZUhKSkJpaWlSEwMInaBICKJ8gLgpa7SbU+VBvfZ97J5cXPV68C5N0n3bXwfWDpd+XPB1k8QBKEjoTy/adwnQZiNJqlATELdPtu8C79k420EhIk75Yz/rG7fRRAEEUZI4BCEGWk/pG6fE0ZSKeXCURM454xS3k4QBBHBkMAhCDMSzYwYvOnb4D8nTLcQigWHEvwRBGFCqOUiCLMTijVHsOAoCZwNb/PLrGn1PyaCIIgwQwKHIMxIjzH8MrmtdKbwQAgWnCMb1MuUHBLXR84N+dAIgiAiAV2HiRMEoRO9xvJiJbV3aJ9LaMYv5Qn83C5x3eX0LU8QBGEySOAQhBmxWICOF4X+uWYdxXWXQ7T+OGvE7WwCQJpBnCAIk0IuKoI4m4hmZiVn555yMALn4sfF9WAm8SQIgohASOAQxNlEtA2AJ+sxa7URJu20xgBNUoC4ZP59Sk8jj44gCEIzyEVFEGcTFgsQHccLGgczRYpgwYnxWHim7+RdWLYmxh8jQRCEBpDAIYizjRhB4DAuKsGCEx3HL4OZvJMgCCKCIRcVQZxtCHE1Ncz8Ul4LTpzxx0MQBKEDJHAI4mwj3jP0u6pY3HbwN35po6BigiAaBiRwCOJsQxj6XX1G3FZbyS+rThl/PARBEDpAAocgzjaE5H3VjAXHaeeXva8z/ngIgiB0gAQOQZxtKLmoXLX8kp3EkyAIwsSQwCGIsw1/FpwoEjgEQTQMSOAQxNmGkgVHEDjRscYfD0EQhA6QwCGIsw2vBYcJMnaRBYcgiIYFCRyCONuIT+aXrMChPDgEQTQwSOAQxNmG4KIqLxC3CWKHZg8nCKKBQAKHIM42BBdV1Smg7AS/LgQcC+KHIAjC5JDAIYizDVbErHmVXwoBx2TBIQiigUAChyDONtgZwsuOARwnuqgSyIJDEETDgAQOQZxtWCzienJbwF4GcC7+PbmoCIJoIJDAIYizkZ7X8suaUtE9FRVLo6gIgmgwRIf7AAiCCAONWvLLLR8D1ih+XZiugSAIogFAFhyCOBuprRDX8xaG7TAIgiD0ggQOQZyNWOjWJwiiYUOtHEGcjSgJnBFPG38cBEEQOkEChyDORlp29d3WNsv44yAIgtAJ3QTOM888g8GDByMhIQHJyclBfYbjOMyaNQtpaWmIj49HdnY29u7dKylTXFyMCRMmIDExEcnJyZg8eTIqKipUaiQIQpHzpvhua5Jq/HEQBEHohG4Cp7a2Ftdddx3uvPPOoD/z/PPPY968eXjrrbewfv16NGrUCCNHjkRNTY23zIQJE7Bjxw4sW7YMP/74I3777TdMnTpVj59AEA2X6Fig03DptsTW4TkWgiAIHbBwHMfp+QULFy7E/fffj5KSEr/lOI5Deno6HnzwQTz00EMAgNLSUqSkpGDhwoUYP348du3ahR49emDjxo0YOHAgACAnJweXX345jh49ivT09KCOqaysDElJSSgtLUViYmK9fh9BmJZn2wE1JeL7p0rDdigEQRDBEMrzO2JicA4cOICCggJkZ2d7tyUlJSEzMxNr164FAKxduxbJyclecQMA2dnZsFqtWL9+vWrddrsdZWVlkhdBnPVccF+4j4AgCEI3IkbgFBQUAABSUlIk21NSUrz7CgoK0KpVK8n+6OhoNGvWzFtGiblz5yIpKcn7ysjI0PjoCcKEnH+XuH6XegeBIAjCjIQkcGbMmAGLxeL3tXv3br2Otc7MnDkTpaWl3teRI0fCfUgEEX5i4oAbvwb+tQBo1S3cR0MQBKEpIU3V8OCDD+KWW27xW6Zjx451OpDUVH4ER2FhIdLS0rzbCwsL0a9fP2+ZoqIiyeecTieKi4u9n1fCZrPBZrPV6bgIokHTOTtwGYIgCBMSksBp2bIlWrZsqcuBdOjQAampqcjNzfUKmrKyMqxfv947EisrKwslJSXIy8vDgAEDAAArVqyA2+1GZmamLsdFEARBEIT50C0G5/Dhw8jPz8fhw4fhcrmQn5+P/Px8Sc6abt26YcmSJQAAi8WC+++/H08//TS+//57bNu2DRMnTkR6ejrGjBkDAOjevTtGjRqFKVOmYMOGDVizZg2mTZuG8ePHBz2CiiAIgiCIho9us4nPmjULH374ofd9//79AQArV67EsGHDAAB79uxBaak4NPWRRx5BZWUlpk6dipKSEgwZMgQ5OTmIi4vzllm0aBGmTZuG4cOHw2q1YuzYsZg3b55eP4MgCIIgCBOiex6cSITy4BAEQRCE+TBlHhyCIAiCIAitIIFDEARBEESDgwQOQRAEQRANDhI4BEEQBEE0OEjgEARBEATR4CCBQxAEQRBEg4MEDkEQBEEQDQ4SOARBEARBNDhI4BAEQRAE0eDQbaqGSEZI3lxWVhbmIyEIgiAIIliE53YwkzCclQKnvLwcAJCRkRHmIyEIgiAIIlTKy8uRlJTkt8xZOReV2+3G8ePH0aRJE1gsFk3rLisrQ0ZGBo4cOULzXOkInWdjoPNsDHSejYHOs3Hoda45jkN5eTnS09NhtfqPsjkrLThWqxVt2rTR9TsSExPpBjIAOs/GQOfZGOg8GwOdZ+PQ41wHstwIUJAxQRAEQRANDhI4BEEQBEE0OEjgaIzNZsOTTz4Jm80W7kNp0NB5NgY6z8ZA59kY6DwbRySc67MyyJggCIIgiIYNWXAIgiAIgmhwkMAhCIIgCKLBQQKHIAiCIIgGBwkcgiAIgiAaHCRwNGT+/Plo37494uLikJmZiQ0bNoT7kCKa3377DVdeeSXS09NhsVjw7bffSvZzHIdZs2YhLS0N8fHxyM7Oxt69eyVliouLMWHCBCQmJiI5ORmTJ09GRUWFpMzWrVsxdOhQxMXFISMjA88//7zePy2imDt3Ls477zw0adIErVq1wpgxY7Bnzx5JmZqaGtx9991o3rw5GjdujLFjx6KwsFBS5vDhwxg9ejQSEhLQqlUrPPzww3A6nZIyq1atwrnnngubzYbOnTtj4cKFev+8iOHNN99Enz59vInNsrKy8PPPP3v30znWh2effRYWiwX333+/dxud6/rz1FNPwWKxSF7dunXz7jfFOeYITVi8eDEXGxvLLViwgNuxYwc3ZcoULjk5mSssLAz3oUUsP/30E/ef//yH++abbzgA3JIlSyT7n332WS4pKYn79ttvub/++ou76qqruA4dOnDV1dXeMqNGjeL69u3LrVu3jvv999+5zp07czfccIN3f2lpKZeSksJNmDCB2759O/fZZ59x8fHx3Ntvv23Uzww7I0eO5D744ANu+/btXH5+Pnf55Zdzbdu25SoqKrxl7rjjDi4jI4PLzc3lNm3axJ1//vnc4MGDvfudTifXq1cvLjs7m9uyZQv3008/cS1atOBmzpzpLbN//34uISGBmz59Ordz507utdde46KioricnBxDf2+4+P7777mlS5dyf//9N7dnzx7uscce42JiYrjt27dzHEfnWA82bNjAtW/fnuvTpw933333ebfTua4/Tz75JNezZ0/uxIkT3tfJkye9+81wjkngaMSgQYO4u+++2/ve5XJx6enp3Ny5c8N4VOZBLnDcbjeXmprKvfDCC95tJSUlnM1m4z777DOO4zhu586dHABu48aN3jI///wzZ7FYuGPHjnEcx3FvvPEG17RpU85ut3vLPProo1zXrl11/kWRS1FREQeAW716Ncdx/HmNiYnhvvzyS2+ZXbt2cQC4tWvXchzHi1Gr1coVFBR4y7z55ptcYmKi99w+8sgjXM+ePSXfNW7cOG7kyJF6/6SIpWnTptx7771H51gHysvLuS5dunDLli3jLrroIq/AoXOtDU8++STXt29fxX1mOcfkotKA2tpa5OXlITs727vNarUiOzsba9euDeORmZcDBw6goKBAck6TkpKQmZnpPadr165FcnIyBg4c6C2TnZ0Nq9WK9evXe8tceOGFiI2N9ZYZOXIk9uzZgzNnzhj0ayKL0tJSAECzZs0AAHl5eXA4HJJz3a1bN7Rt21Zyrnv37o2UlBRvmZEjR6KsrAw7duzwlmHrEMqcjfeAy+XC4sWLUVlZiaysLDrHOnD33Xdj9OjRPueDzrV27N27F+np6ejYsSMmTJiAw4cPAzDPOSaBowGnTp2Cy+WS/JEAkJKSgoKCgjAdlbkRzpu/c1pQUIBWrVpJ9kdHR6NZs2aSMkp1sN9xNuF2u3H//ffjggsuQK9evQDw5yE2NhbJycmSsvJzHeg8qpUpKytDdXW1Hj8n4ti2bRsaN24Mm82GO+64A0uWLEGPHj3oHGvM4sWLsXnzZsydO9dnH51rbcjMzMTChQuRk5ODN998EwcOHMDQoUNRXl5umnN8Vs4mThBnK3fffTe2b9+OP/74I9yH0iDp2rUr8vPzUVpaiq+++go333wzVq9eHe7DalAcOXIE9913H5YtW4a4uLhwH06D5bLLLvOu9+nTB5mZmWjXrh2++OILxMfHh/HIgocsOBrQokULREVF+USQFxYWIjU1NUxHZW6E8+bvnKampqKoqEiy3+l0ori4WFJGqQ72O84Wpk2bhh9//BErV65EmzZtvNtTU1NRW1uLkpISSXn5uQ50HtXKJCYmmqZBrC+xsbHo3LkzBgwYgLlz56Jv37549dVX6RxrSF5eHoqKinDuueciOjoa0dHRWL16NebNm4fo6GikpKTQudaB5ORknHPOOdi3b59prmcSOBoQGxuLAQMGIDc317vN7XYjNzcXWVlZYTwy89KhQwekpqZKzmlZWRnWr1/vPadZWVkoKSlBXl6et8yKFSvgdruRmZnpLfPbb7/B4XB4yyxbtgxdu3ZF06ZNDfo14YXjOEybNg1LlizBihUr0KFDB8n+AQMGICYmRnKu9+zZg8OHD0vO9bZt2ySCctmyZUhMTESPHj28Zdg6hDJn8z3gdrtht9vpHGvI8OHDsW3bNuTn53tfAwcOxIQJE7zrdK61p6KiAv/88w/S0tLMcz1rEqpMcIsXL+ZsNhu3cOFCbufOndzUqVO55ORkSQQ5IaW8vJzbsmULt2XLFg4A9/LLL3NbtmzhDh06xHEcP0w8OTmZ++6777itW7dyV199teIw8f79+3Pr16/n/vjjD65Lly6SYeIlJSVcSkoKd9NNN3Hbt2/nFi9ezCUkJJxVw8TvvPNOLikpiVu1apVkyGdVVZW3zB133MG1bduWW7FiBbdp0yYuKyuLy8rK8u4XhnyOGDGCy8/P53JycriWLVsqDvl8+OGHuV27dnHz588/q4bVzpgxg1u9ejV34MABbuvWrdyMGTM4i8XC/frrrxzH0TnWE3YUFcfRudaCBx98kFu1ahV34MABbs2aNVx2djbXokULrqioiOM4c5xjEjga8tprr3Ft27blYmNjuUGDBnHr1q0L9yFFNCtXruQA+LxuvvlmjuP4oeJPPPEEl5KSwtlsNm748OHcnj17JHWcPn2au+GGG7jGjRtziYmJ3KRJk7jy8nJJmb/++osbMmQIZ7PZuNatW3PPPvusUT8xIlA6xwC4Dz74wFumurqau+uuu7imTZtyCQkJ3DXXXMOdOHFCUs/Bgwe5yy67jIuPj+datGjBPfjgg5zD4ZCUWblyJdevXz8uNjaW69ixo+Q7Gjq33nor165dOy42NpZr2bIlN3z4cK+44Tg6x3oiFzh0ruvPuHHjuLS0NC42NpZr3bo1N27cOG7fvn3e/WY4xxaO4zhtbEEEQRAEQRCRAcXgEARBEATR4CCBQxAEQRBEg4MEDkEQBEEQDQ4SOARBEARBNDhI4BAEQRAE0eAggUMQBEEQRIODBA5BEARBEA0OEjgEQRAEQTQ4SOAQBEEQBNHgIIFDEARBEESDgwQOQRAEQRANDhI4BEEQBEE0OP4f02N+N6eJm3YAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(signal[:, 0])\n",
    "scaler = MinMaxScaler((-1, 1))\n",
    "\n",
    "plt.plot(scaler.fit_transform(signal)[:, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b21f01d3-ad8d-4779-a27b-f141a1c850ca",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# headers = glob.glob(data_path+'*.hea')\n",
    "# headers = [h[:-4] for h in headers] # remove extensions\n",
    "# unique_labels = collections.defaultdict(int)\n",
    "\n",
    "# for f in headers:\n",
    "#     signal, meta = wfdb.rdsamp(f)\n",
    "\n",
    "#     # Label\n",
    "#     l = meta['comments'][2]\n",
    "#     for sc in l[4:].split(','):\n",
    "#         unique_labels[sc] += 1\n",
    "\n",
    "# unique_labels = {k:v for k, v in sorted(unique_labels.items(), key = lambda i: i[1], reverse=True)}\n",
    "# unique_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "5309f058-8c82-467c-bbe9-dd45ebc8e82c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10344"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # headers = glob.glob(data_path+'*.hea')\n",
    "# # headers = [h[:-4] for h in headers] # remove extensions\n",
    "# length = []\n",
    "# for f in headers:\n",
    "#     signal, meta = wfdb.rdsamp(f)\n",
    "#     length.append(len(signal))\n",
    "\n",
    "# len(length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b7fe862-705a-4e74-93f3-4066b2c7f3e3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1a07fe87-f964-4c9a-8ccb-15b0a3ebf8d9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def downsample(signal, ratio=5):\n",
    "    # Signal of size (L, 12) to (L/ratio, 12)\n",
    "    return signal[::ratio, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "88f9f48b-24fc-43b9-9e49-761153b4bff5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "LENGTH = 1000\n",
    "def crop(signal):\n",
    "    return signal[0:LENGTH, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "02944923-d422-4258-bb1c-4152d13013d1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def load_raw_data(path):\n",
    "    headers = glob.glob(path+'*.hea')\n",
    "    headers = [h[:-4] for h in headers] # remove extensions\n",
    "    data = []\n",
    "    labels = []\n",
    "    scaler = MinMaxScaler((-1, 1))\n",
    "    \n",
    "    for f in tqdm(headers):\n",
    "        signal, meta = wfdb.rdsamp(f)\n",
    "        \n",
    "        # Signal\n",
    "        downsampled = downsample(signal)\n",
    "        l = downsampled.shape[0]\n",
    "        if l < LENGTH: # drop 6 signals of length < 1000\n",
    "            continue\n",
    "        elif l > LENGTH:\n",
    "            downsampled = crop(downsampled)\n",
    "            l = len(downsampled)\n",
    "            assert l == LENGTH\n",
    "        \n",
    "        # Min-max scaling\n",
    "        scaled = scaler.fit_transform(downsampled)\n",
    "        \n",
    "        data.append(scaled)\n",
    "        \n",
    "        # Label\n",
    "        l = meta['comments'][2]\n",
    "        assert l.startswith('Dx:') and l[3] == ' '\n",
    "        lb = [0] * len(CLASSES)\n",
    "        # Set one-hot values for diagnosed labels\n",
    "        for sc in l[4:].split(','):\n",
    "            if sc not in SNOMED_CODES:\n",
    "                continue\n",
    "            val = SNOMED_CODES[sc]\n",
    "            idx = CLASSES.index(val)\n",
    "            lb[idx] = 1\n",
    "            \n",
    "        labels.append(lb)\n",
    "        \n",
    "    assert len(data) == len(labels)\n",
    "    return np.array(data), np.array(labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8b2aa89b-4bae-4cdb-ba59-0cf2839a439a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████| 10344/10344 [00:28<00:00, 363.64it/s]\n"
     ]
    }
   ],
   "source": [
    "X, y = load_raw_data(data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0970a566-cce2-4dc3-b962-cdc5e8eecaac",
   "metadata": {},
   "source": [
    "### Split "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e1be65d3-66fa-451b-8c7d-72dfbcb804d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((10292, 1000, 12), (10292, 10))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "65e8d416-d499-4625-9dec-707d060b811f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "05a30356-2c16-4b35-8fa8-b68a1054a7b5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.17171864, 0.05815809, 0.07382161, 0.16113125, 0.09079043,\n",
       "       0.11921682, 0.22480058, 0.08092821, 0.13604061, 0.06192893])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.sum(axis=0)/len(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "070a600c-a9b5-4cd9-af4f-16d4dd5faf59",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.16220194, 0.04916102, 0.07565499, 0.16396821, 0.09184575,\n",
       "       0.12923168, 0.22048867, 0.07359435, 0.13099794, 0.06152487])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.sum(axis=0)/len(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0b1f472-9b60-443e-b33c-eece898c42a1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fa10aca4-54e2-4fa7-a59a-01c923c572d7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6895, 1000, 12)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fb4943af-5908-4eb6-93ed-7957c1101170",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10,)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8f45cf4-3bcf-4cf5-b932-3ff24d73f1c6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "27b5432e-70dd-427b-8285-1dc5fa4639ab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# X_test = joblib.load('./data/X_test.joblib')\n",
    "np.isnan(X_test).any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d4d8386b-3b57-4fd2-9e0a-ad9269f2613c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# X_train = joblib.load('./data/X_train.joblib')\n",
    "np.isnan(X_train).any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "89f35006-41be-4eb8-9408-2ce00044159a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 289,  250,    0],\n",
       "       [ 289,  250,    1],\n",
       "       [ 289,  403,    0],\n",
       "       [ 289,  403,    1],\n",
       "       [ 289,  648,    0],\n",
       "       [ 289,  648,    1],\n",
       "       [ 289,  828,    0],\n",
       "       [ 289,  828,    1],\n",
       "       [ 488,    0,    8],\n",
       "       [ 488,    1,    8],\n",
       "       [ 623,  908,   11],\n",
       "       [4223,   42,    7],\n",
       "       [4430,  746,    7],\n",
       "       [4430,  746,    8],\n",
       "       [4430,  747,    8]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nan_idx_train = np.argwhere(np.isnan(X_train))\n",
    "nan_idx_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "56d9d1ff-4af3-46ed-a4db-8c870da10712",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2309,  358,   11]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nan_idx_test = np.argwhere(np.isnan(X_test))\n",
    "nan_idx_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "410240a2-2591-4247-844b-8628e826c5ae",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2309"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i, j, k = nan_idx_test[0]\n",
    "i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "821fa22d-fa57-4b88-8732-aed9da2cf4ad",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def fill_nan(a, nan_idx): # Won't work with adjacent nan\n",
    "    i, j, k = nan_idx\n",
    "    if j == 0:    \n",
    "        a[i, j, k] = a[i, j+1, k]\n",
    "    elif j == LENGTH-1:\n",
    "        a[i, j, k] = a[i, j-1, k]\n",
    "    else:\n",
    "        a[i, j, k] = (a[i, j+1, k] + a[i, j-1, k]) / 2.0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e01d3e6d-9ca2-4ac3-92c9-5dc455432bba",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for idx in nan_idx_train:\n",
    "    fill_nan(X_train, idx)\n",
    "\n",
    "for idx in nan_idx_test:\n",
    "    fill_nan(X_test, idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "566e5b91-fcfb-4e95-a5ab-26b28fc8fa31",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([], shape=(0, 3), dtype=int64)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argwhere(np.isnan(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0b8b6ba6-1cb5-4133-9e44-c8675543bc22",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 488,    0,    8],\n",
       "       [ 488,    1,    8],\n",
       "       [4430,  746,    8],\n",
       "       [4430,  747,    8]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argwhere(np.isnan(X_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4c81dc4b-b3ff-4319-920b-57d33ca452a5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# For the 4 indices above, fill manually\n",
    "\n",
    "X_train[488,    0,    8] = X_train[488,    2,    8]\n",
    "X_train[488,    1,    8] = X_train[488,    2,    8]\n",
    "X_train[4430,  746,    8] = (X_train[4430,  745,    8] + X_train[4430,  748,    8]) /2.0\n",
    "X_train[4430,  747,    8] = (X_train[4430,  745,    8] + X_train[4430,  748,    8]) /2.0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bc435c01-2208-4c2a-bb4e-fda4ed16ebf2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([], shape=(0, 3), dtype=int64)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argwhere(np.isnan(X_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "676c1ca8-91ba-4308-b998-f1af3722d799",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./data/y_test.joblib']"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(X_train, './data/X_train.joblib')\n",
    "joblib.dump(y_train, './data/y_train.joblib')\n",
    "joblib.dump(X_test, './data/X_test.joblib')\n",
    "joblib.dump(y_test, './data/y_test.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "912c5b52-579d-4c2f-aacb-b73d6602ecb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd95116efd0>]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X_test[19, :, 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2cc134a8-3053-497a-93f2-bccb4cf6cd38",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5134450-05df-4629-b0cb-492f4ad95bcf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e80c8cf6-3535-4781-ad4b-2b9358619906",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((10292, 1000, 12), (10292, 10))"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = joblib.load('./data/X_train.joblib')\n",
    "y_train = joblib.load('./data/y_train.joblib')\n",
    "X_test = joblib.load('./data/X_test.joblib')\n",
    "y_test = joblib.load('./data/y_test.joblib')\n",
    "X_full = np.concatenate((X_train, X_test))\n",
    "y_full = np.concatenate((y_train, y_test))\n",
    "X_full.shape, y_full.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "deb881cb-1662-491d-9817-93ebea7b40a7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./data/y_full.joblib']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(X_full, './data/X_full.joblib')\n",
    "joblib.dump(y_full, './data/y_full.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa6321b6-d804-4219-bd62-4c94c9f8cfef",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ecg",
   "language": "python",
   "name": "ecg"
  },
  "language_info": {
   "codemirror_mode": {
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